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<p>Single-cell RNA sequencing (scRNA-seq) has become an indispensable tool in prostate biology research, considerably advancing our understanding from organ development to disease initiation and progression. This technology has provided profound insights into prostate disease, from benign prostatic hyperplasia to metastatic prostate cancer, particularly with respect to the tumour microenvironment, cellular lineage plasticity and resistance to androgen receptor-dependent therapies. scRNA-seq has revealed complex and potentially targetable interactions between epithelial, stromal and immune subtypes driving disease heterogeneity. As a complementary technology, spatial transcriptomics enables the characterization of tumour architecture using spatial resolution at the single-cell or multi-cell level. Importantly, these approaches also permit bioinformatic inference of large-scale copy number variants, enabling integrated genomic–transcriptomic analysis of clonal evolution and therapy resistance. Understanding the biological consequences of this heterogeneity could support patient sub-stratification, biomarker development and therapeutic strategies targeting genomic instability or tumour microenvironment interactions.</p>

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Single-cell and spatial RNA sequencing in prostate cancer

  • 期刊论文

  • 发表日期：

    2026年5月28日

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  • Amin Ali,
  • Migle Mikutenaite,
  • Joachim Weischenfeldt,
  • Robert G. Bristow

摘要

Single-cell RNA sequencing (scRNA-seq) has become an indispensable tool in prostate biology research, considerably advancing our understanding from organ development to disease initiation and progression. This technology has provided profound insights into prostate disease, from benign prostatic hyperplasia to metastatic prostate cancer, particularly with respect to the tumour microenvironment, cellular lineage plasticity and resistance to androgen receptor-dependent therapies. scRNA-seq has revealed complex and potentially targetable interactions between epithelial, stromal and immune subtypes driving disease heterogeneity. As a complementary technology, spatial transcriptomics enables the characterization of tumour architecture using spatial resolution at the single-cell or multi-cell level. Importantly, these approaches also permit bioinformatic inference of large-scale copy number variants, enabling integrated genomic–transcriptomic analysis of clonal evolution and therapy resistance. Understanding the biological consequences of this heterogeneity could support patient sub-stratification, biomarker development and therapeutic strategies targeting genomic instability or tumour microenvironment interactions.