The recent advent of spatial transcriptomic technologies and single-cell sequencing has the potential to revolutionize our understanding of complex diseases with unprecedented insights into the molecular and cellular heterogeneity of tissues. Traditional bulk RNA sequencing has been instrumental in identifying key genes and pathways associated with different disease states; however, this approach averages gene expression signals across all cells within a tissue sample, masking the underlying cellular heterogeneity and cell-to-cell interactions.^1-3 Spatial transcriptomics, highlighted as Nature Methods 2021 technology of the year, allows for the investigation of gene expression patterns within intact tissue sections while maintaining spatial context. By characterizing the spatial distribution of diseased or tumor cells, immune and stromal cells, as well as other components, researchers can unravel the intricate interactions and communication networks within a tissue. Spatial resolution is particularly important in the study of heterogenous and complex diseases like prostate cancer. In this article, we describe the applications of spatial transcriptomics and single-cell sequencing in urological research with special emphasis on prostate cancer.

One spatial transcriptomic platform is digital spatial profiling (DSP) by NanoString Technologies. It utilizes a combination of spatially barcoded oligonucleotides and digital counting to quantify RNA molecules in regions of interest (ROIs). The tissue sample is sectioned onto a slide and ROIs are identified based on their spatial location, relevant histology, and/or morphology markers (see Figure). Barcoded oligonucleotide probes called GeoMx DSP spatial capture agents are hybridized to the tissue to capture and amplify RNA transcripts within the ROI. The captured RNA is then detected using fluorescently labeled reporter probes and imaged using a fluorescence microscope. This digital counting approach allows for precise quantification of gene expression levels in each region (see part B of Figure).¹ To understand the molecular differences between peripheral zone (PZ) and transition zone (TZ) prostate cancers, our group utilized DSP to analyze tumor samples obtained from 3 patients who underwent radical prostatectomy for prostate cancer. We analyzed 50 ROIs from PZ and TZ with a total capture of 17,128 genes. Differential gene expression and pathway enrichment analyses revealed that androgen response signaling was upregulated in TZ tumors compared to PZ tumors. With the capacity of the DSP to segment ROI into areas of interest, we localized the enrichment of androgen response signaling to the epithelium. Taken together, these findings provide insights into the biologic differences between PZ and TZ prostate cancers.

Figure. A, Hematoxylin and eosin (H&E) image of radical prostatectomy specimen displaying prostate cancer in the peripheral zone. B, Immunofluorescence (IF) image using morphology markers to delineate the nucleus (SYTO13—blue), epithelium (PanCK—green), stroma/smooth muscle (SMA/ACTA2—yellow), and immune cell (CD45—red) components of the tumor. The H&E and IF images are then combined to select regions of interest (ROIs; 7 ROIs in this case) and segment ROIs into areas of interest (AOIs; 12 AOIs in this case) for digital spatial profiling using the NanoString Technologies platform.

Another spatial platform is Visium Spatial Gene Expression by 10x Genomics. This platform uses demarked regions on a slide with thousands of spots per region where each spot contains millions of mRNA capture probes with a barcode unique to that spot. The tissue specimen is laid over the slide and solubilized so that the overlying mRNA is captured in each spot and then sequenced. Our group utilized Visium to elucidate the transcriptomic changes that occur in the prostate over time after orchiectomy in association with changes in the tissue architecture. We orchiectomized mice and then performed Visium spatial transcriptomics on the prostate at days 10, 15, and 20 in comparison to the sham. We also obtained single cell RNAseq from the prostates of 2 additional mice at days 0 and 15 post-orchiectomy to provide true single-cell resolution and found good concordance between the single-cell and Visium spatial findings, which allowed mapping of the single-cell data onto the spatial transcriptomic data. We found notable changes in androgen response genes that varied between prostate lobes as well as drastic changes in immune cell regulation and cell motility.

Characterization of each cell in a tumor may be needed to truly understand and potentially overcome the issue imposed by heterogeneity. The above spatial transcriptomic platforms provide spatially resolved information for very small areas or regions ranging from a few to hundreds of cells.^1-2 Until very recently, the lack of single-cell or subcellular resolution has been a limitation for certain applications in spatial technology. In parallel to the development of spatial platforms, single-cell sequencing has emerged as a powerful technique to analyze individual cells within a sample, providing detailed insights into cellular diversity and heterogeneity.⁴ By profiling the transcriptome of individual cells, researchers can identify rare cell populations, characterize cell states, and uncover cell-to-cell variability. Single-cell sequencing can be performed using several technologies, such as droplet-based methods like Drop-seq or Chromium Systems by 10x Genomics, or plate-based methods like Smart-seq. Applying single-cell sequencing to prostate cancer research has enabled the identification and characterization of rare cell populations, such as cancer stem cells or therapy-resistant cells, which play crucial roles in tumor initiation, progression, and treatment resistance. By dissecting the molecular features of these cells, researchers can develop targeted therapies to eliminate or inhibit their growth, thereby improving treatment outcomes.^4-6 Moreover, single-cell sequencing has provided insights into the heterogeneity of cancer-associated immune cells within prostate tumors. Immune cell populations, such as T cells, macrophages, and dendritic cells, can exhibit diverse functional states and phenotypes within the tumor microenvironment. Understanding this complexity is crucial for developing immunotherapies and optimizing treatment strategies.⁷

The field of molecular profiling technology and techniques continues to evolve rapidly. New technologies incorporating both single-cell and spatial resolution have begun to emerge. One such platform is the NanoString CosMx spatial molecular imager, which uses in situ hybridization of barcoded mRNA probes with multiple rounds of reporter probe hybridization to produce subcellular level transcript localization. The platform, however, currently has a limitation of a 1,000-plex gene panel, though this is expected to increase over time. Our group utilized CosMx to explore the sarcomatoid transformation in renal cell carcinoma, which is thought to occur through an epithelial-to-mesenchymal transition. Both the single-cell and spatial resolution were crucial to our ability to detect a novel cell state along the epithelial-to-mesenchymal transition continuum as well as key interactions between the transitioning cells, macrophages, and CD8 T-cells. We believe this will lead to new biomarkers for immunotherapy response and potentially new therapeutic targets in kidney cancer. Critically, this integrative approach holds great promise for identifying novel biomarkers and therapeutic targets in prostate cancer.

Spatial transcriptomics and single-cell sequencing have already begun to revolutionize our understanding of malignancies including prostate cancer. These cutting-edge technologies provide insights into distinct cell populations, rare cell types, and cellular interactions in the tumor microenvironment. Such approaches open new avenues for discovery in the field and hold great promise for improving diagnosis, prognosis, and treatment strategies, leading to more personalized and effective therapies that target the dominant clones in cancer, the needle in a haystack. Moreover, advancements in spatial proteomic platforms and 3D multi-omics techniques are continuously evolving, offering exciting new possibilities. However, it is essential to carefully consider the necessity and suitability of these expensive technologies for addressing specific research inquiries as well as thoughtful integration into clinical care paradigms. With deliberate application, spatial biology has the potential to transform translational medicine, and we have only begun to scratch the surface of its capabilities.