Single-cell transcriptome analysis of mesenchymal and epithelial cells from four patients with polydactyly in the GEO public database

doi:10.12307/2025.687

Abstract

Abstract: BACKGROUND: Aberrant expression of some transcription factors involved in the Heghog signaling pathway occurs in patients with polydactyly, and these transcription factors regulate the expression of massive target genes, thereby affecting cellular function.
OBJECTIVE: To analyze the transcriptome characterization of patients with polydactyly by single-cell transcriptome analysis.
METHODS: The single-cell transcriptome data of mesenchymal and epithelial cells of four patients with polydactyly were downloaded from the GEO public database. Fibroblasts and keratinocytes were categorized into cell subsets, and the transcription factors within each subset were examined. A regulatory network of these transcription factors and their target genes was developed, and the functions of these regulatory factors were analyzed.
RESULTS AND CONCLUSION: The transcriptional profiling data of individual cells indicated that the regulatory factors strongly linked to cell functionality in polydactyly include the transcription factors HOXD13, MSX2, LHX2, EMX2, LEF1, CREB3L2, and LHX2 of the HOX family and GLI2 transcription factors. In fibroblasts, HOXD13, MSX2, and LHX2 are involved in polydactyly, whereas HES2 and GLIS1 are crucial for the formation and development of keratinocytes. To conclude, the elevated expression of transcription factors including HOXD13, MSX2, and LHX2 is potentially closely associated with the development of polydactyly. Potential therapeutic strategies that may be offered to correct or prevent polydactyly by targeting specific transcription factors or modulating their activity.

中国组织工程研究杂志出版内容重点：组织构建；骨细胞；软骨细胞；细胞培养；成纤维细胞；血管内皮细胞；骨质疏松；组织工程

Key words: polydactyly, transcription factors, bioinformatics, single-cell transcriptome analysis, microenvironment, Heghog signaling pathway, bid data analysis

CLC Number:

Fu Dongsheng, , Aikeremujiang • Muheremu, . Single-cell transcriptome analysis of mesenchymal and epithelial cells from four patients with polydactyly in the GEO public database[J]. Chinese Journal of Tissue Engineering Research, 2025, 29(20): 4379-4388.

Figures/Tables 12

2.1 多指畸形患者单细胞RNA-seq分析鉴定不同类型的细胞为了更深入地探索多指畸形发展中细胞特异性的关键调控因子，下载GSE158970多指畸形样本的数据，包括4例多指畸形患者的细胞间质和上皮细胞的单细胞数据。首先，在严格的数据质量控制后，获得了11 806个单细胞的转录谱数据。对这11 806个单细胞的转录表达矩阵进行了归一化处理和主成分分析，并选择前50个主成分进行统一流形逼近与投影(uniform manifold approximation and projection，UMAP)降维和可视化。经过无偏聚类分析，获得了23个细胞亚群(聚类)。使用sctype软件并结合原始文献注释中使用的标记基因，鉴定出了12个不同的细胞类型(图1A，B，图2)。同时，还显示了每个细胞类型的前3个标记基因的表达和富集功能通路(|avg_log2FC|≥0.5，P_val_adj≤0.05)(图1C，D)。发现不同细胞类型的标记基因和富集功能存在显著差异。例如，黑素细胞的显著标记基因主要与黑素细胞分化、黑素囊泡组织和黑色素生物合成相关，表明黑素细胞在黑色素分泌和皮肤色素沉着中可能起着重要作用。接下来，分析了每个样本中每种细胞类型的比例(图1E)，发现主要的细胞类型包括成纤维细胞、角质细胞和内皮细胞。 2.2 单细胞分析揭示了与多指畸形细胞功能高度相关的调控因子为了调查多指畸形不同细胞类型调控因子的调控情况，计算了各种细胞群体中转录因子的活性。分别鉴定了在成纤维细胞和角质细胞中特异激活的14个和4个调控因子。图3A，B分别显示了成纤维细胞和角质细胞相关调控因子的特异性得分。成纤维细胞中的调控因子HOXD13(+)和GLI2(+)在AUC评分上更高，并且这两个转录因子的表达在成纤维细胞中也更具特异性。角质细胞中的调控因子HES2(+)和GLIS1(+)在AUC评分上较高，这两个转录因子的表达在角质细胞中也相对特异(图3C)。为了进一步验证这些转录因子的调控功能，构建了转录因子及其靶基因之间的调控网络(图3D)。其中，GLI2在成纤维细胞中调控的靶基因数量很多。GO富集分析显示在突触后膜组装、背腹极化图案形成和细胞迁移通路上富集(图3E)，表明GLI2转录因子可能在多指畸形的形成过程中被激活。此外，HOXD13也被广泛报道与多指畸形的发生和发展密切相关，其突变可以导致人类和小鼠的肢体异常。HOXD13在指间间质中表达，并作为软骨形成和细胞分化的抑制因子。缺乏HOXD13会减少指融合处的厚度和长度，由此引起多指畸形，产生额外的、延长的手指在轴后面。角质细胞中HES2调控的靶基因主要与转录调控有关(图4)，GLIS1调控的靶基因主要与增殖和凋亡通路相关(图3F)，提示它们在维持细胞功能方面发挥作用。"

References

[1] AHMAD Z, LIAQAT R, PALANDER O, et al. Genetic overview of postaxial polydactyly: Updated classification. Clin Genet. 2023; 103(1):3-15.
[2] BUBSHAIT DK. A review of polydactyly and its inheritance: Connecting the dots. Medicine (Baltimore). 2022;101(50): e32060.
[3] KYRIAZIS Z, KOLLIA P, GRIVEA I, et al. Polydactyly: Clinical and molecular manifestations. World J Orthop. 2023;14(1): 13-22.
[4] ÁLVAREZ LFG, TENORIO-CASTAÑO J, POLETTA FA, et al. A large, ten-generation family with autosomal dominant preaxial polydactyly/triphalangeal thumb: Historical, clinical, genealogical, and molecular studies. Am J Med Genet A. 2023;191(1):100-107.
[5] IANEVSKI A, GIRI AK, AITTOKALLIO T. Fully-automated and ultra-fast cell-type identification using specific marker combinations from single-cell transcriptomic data. Nat Commun. 2022;13(1):1246.
[6] LEE J, HYEON DY, HWANG D. Single-cell multiomics: technologies and data analysis methods. Exp Mol Med. 2020;52(9):1428-1442.
[7] VAN DE SANDE B, FLERIN C, DAVIE K, et al. A scalable SCENIC workflow for single-cell gene regulatory network analysis. Nat Protoc. 2020;15(7):2247-2276.
[8] LIU S, WANG Z, ZHU R, et al. Three Differential Expression Analysis Methods for RNA Sequencing: limma, EdgeR, DESeq2. J Vis Exp. 2021;(175):e62528.
[9] MA A, WANG C, CHANG Y, et al. IRIS3: integrated cell-type-specific regulon inference server from single-cell RNA-Seq. Nucleic Acids Res. 2020;48(W1): W275-W286.
[10] BU D, LUO H, HUO P, et al. KOBAS-i: intelligent prioritization and exploratory visualization of biological functions for gene enrichment analysis. Nucleic Acids Res. 2021;49(W1):W317-W325.
[11] MITSIS T, EFTHIMIADOU A, BACOPOULOU F, et al. Transcription factors and evolution: an integral part of gene expression. World Acad Sci J. 2020;2(1):3-8.
[12] LOO CKC, PEAREN MA, RAMM GA. The Role of Sonic Hedgehog in Human Holoprosencephaly and Short-Rib Polydactyly Syndromes. Int J Mol Sci. 2021; 22(18):9854.
[13] HAMELIN A, CONCHOU F, FUSELLIER M, et al. Genetic heterogeneity of polydactyly in Maine Coon cats. J Feline Med Surg.2020;22(12):1103-1113.
[14] XU C, YANG X, ZHOU H, et al. A novel ZRS variant causes preaxial polydactyly type I by increased sonic hedgehog expression in the developing limb bud. Genet Med. 2020;22(1):189-198.
[15] JIAO H, WALCZAK BE, LEE MS, et al. GATA6 regulates aging of human mesenchymal stem/stromal cells. Stem Cells. 2021;39(1): 62-77.
[16] YIN J, GUO Y. HOXD13 promotes the malignant progression of colon cancer by upregulating PTPRN2. Cancer Med. 2021; 10(16):5524-5533.
[17] DESALVO J, BAN Y, LI L, et al. ETV4 and ETV5 drive synovial sarcoma through cell cycle and DUX4 embryonic pathway control. J Clin Invest. 2021;131(13):e141908.
[18] PAN J, FANG S, TIAN H, et al. lncRNA JPX/miR-33a-5p/Twist1 axis regulates tumorigenesis and metastasis of lung cancer by activating Wnt/β-catenin signaling. Mol Cancer. 2020;19(1):9.
[19] TAN T, XU Z, GAO C, et al. Influence and interaction of resting state functional magnetic resonance and tryptophan hydroxylase-2 methylation on short-term antidepressant drug response. BMC Psychiatry. 2022;22(1):218.
[20] PELED A, SARIG O, MOHAMAD J, et al. Dominant frontonasal dysplasia with ectodermal defects results from increased activity of ALX4. Am J Med Genet A. 2023; 191(12):2806-2812.
[21] BUTLER A, HOFFMAN P, SMIBERT P, et al. Integrating single-cell transcriptomic data across different conditions, technologies, and species. Nat Biotechnol. 2018;36(5):411-420.
[22] RITCHIE ME, PHIPSON B, WU D, et al. limma powers differential expression analyses for RNA-sequencing and microarray studies. Nucleic Acids Res. 2015;43(7):e47.
[23] IANEVSKI A, GIRI AK, AITTOKALLIO T. Fully-automated and ultra-fast cell-type identification using specific marker combinations from single-cell transcriptomic data. Nat Commun. 2022;13(1):1246.
[24] AIBAR S, GONZÁLEZ-BLAS CB, MOERMAN T, et al. SCENIC: single-cell regulatory network inference and clustering. Nat Methods. 2017;14(11):1083-1086.
[25] SUO S, ZHU Q, SAADATPOUR A, et al. Revealing the Critical Regulators of Cell Identity in the Mouse Cell Atlas. Cell Rep. 2018;25(6):1436-1445.e3.
[26] XIE C, MAO X, HUANG J, et al. KOBAS 2.0: a web server for annotation and identification of enriched pathways and diseases. Nucleic Acids Res. 2011;39(Web Server issue): W316-W322.
[27] JOVIC D, LIANG X, ZENG H, et al. Single-cell RNA sequencing technologies and applications: A brief overview. Clin Transl Med. 2022;12(3):e694.
[28] SLOVIN S, CARISSIMO A, PANARIELLO F, et al. Single-Cell RNA Sequencing Analysis: A Step-by-Step Overview. Methods Mol Biol. 2021;2284:343-365.
[29] MEREU E, LAFZI A, MOUTINHO C, et al. Benchmarking single-cell RNA-sequencing protocols for cell atlas projects. Nat Biotechnol. 2020;38(6):747-755.
[30] KIM N, KIM HK, LEE K, et al. Single-cell RNA sequencing demonstrates the molecular and cellular reprogramming of metastatic lung adenocarcinoma. Nat Commun. 2020; 11(1):2285.
[31] ZHANG Y, WANG D, PENG M, et al. Single‐cell RNA sequencing in cancerresearch. J Exp Clin Canc Res. 2021;40:81.
[32] PAIK DT, CHO S, TIAN L, et al. Single-cell RNA sequencing in cardiovascular development, disease and medicine. Nat Rev Cardiol. 2020;17(8):457-473.
[33] LIAO J, YU Z, CHEN Y, et al. Single-cell RNA sequencing of human kidney. Sci Data. 2020;7(1):4.
[34] WU Y, ZHANG K. Tools for the analysis of high-dimensional single-cell RNA sequencing data. Nat Rev Nephrol. 2020; 16(7):408-421.
[35] KIM HJ, SHIM JH, PARK JH, et al. Single-cell RNA sequencing of human nail unit defines RSPO4 onychofibroblasts and SPINK6 nail epithelium. Commun Biol. 2021;4(1):692.