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Applied to patient-derived bladder cancer spheroids, it analyzes heterogeneity in cancer invasion and clarifies relationships with MALAT1 expression.",{"@graph":14,"@context":73},[15,34,56],{"@type":16,"itemListElement":17},"BreadcrumbList",[18,23,27,31],{"item":19,"name":20,"@type":21,"position":22},"https://docshare.wps.com","Home","ListItem",1,{"item":24,"name":25,"@type":21,"position":26},"https://docshare.wps.com/document/","Document",2,{"item":28,"name":29,"@type":21,"position":30},"https://docshare.wps.com/document/research-report/","Research & 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problem does the proposed workflow address?","Question",{"text":63,"@type":64},"It addresses the technological challenge of analyzing time-lapse, multimodal 3D imaging and gene-expression profiles, which currently often requires time-consuming manual quantification.","Answer",{"name":66,"@type":61,"acceptedAnswer":67},"How does the workflow identify invading structures in 3D tumor spheroids?",{"text":68,"@type":64},"It uses a computer vision-based 3D image processing workflow that performs denoising and segmentation of 3D confocal images, then identifies protrusions and detached clusters from the segmented volumes.",{"name":70,"@type":61,"acceptedAnswer":71},"What biological insight is demonstrated using MALAT1?",{"text":72,"@type":64},"The approach is applied to patient-derived tumor spheroids to investigate invasion heterogeneity and how it relates to MALAT1 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nature.com/scientificreports)  \nOPEN  \nThree-dimensional computer vision for exploring heterogeneity in collective Cancer Invasion  \nYanlin Li1,4, Ninghao Zhu2,4, Mona Ahmed2, Julio Urbina1, Tai-Yin Huang3 & Pak Kin Wong2􀀍  \nCollective cancer invasion exhibits a hierarchical structure characterized by leader-follower organization. Dynamic gene expression analysis of invading cells using nanobiosensors within 3D microenvironments provides a valuable means to explore the regulation of leader cells during collective cancer invasion. Nonetheless, the analysis of time-lapse, multimodal images that capture the intricacies of complex invading structures and gene expression profiles in 3D tumor spheroids poses a significant technological challenge. Here, we present a computer vision-based workflow that streamlines the identification of protrusions and detached clusters from 3D tumor spheroids.  \nThis methodology not only discerns invading multicellular structures and quantifies their physical properties, but also captures gene expression patterns associated with these invasive mechanisms using an intracellular nanobiosensor. Consequently, it empowers a systematic exploration of the genotypic and phenotypic heterogeneities inherent in cancer invasion. To illustrate the effectiveness of this approach, we applied it to the analysis of a long noncoding RNA, MALAT1, in tumor spheroids derived from patients with muscle-invasive bladder cancer. Our investigation delved into the heterogeneity of cancer invasion and its relationship to MALAT1 expression. Overall, this workflow represents a valuable tool for gaining insights into the complexities of cancer invasion.  \nCancer metastasis is responsible for over 90% of cancer-related deaths1. It is increasingly recognized that collective cancer invasion is a pivotal mechanism for metastasis2. During collective cancer invasion, specialized cancer cells at the invading front of the tumor, referred to as leader cells, penetrate the surrounding extracellular matrix, coordinate follower cells, and promote tumor survival in the metastatic cascade3. These leader cells can be identified by their aggressive morphologies, often characterized by an elongated cell shape with active invadopodia4. The formation of leader cells is known to be influenced by various genetic factors and microenvironment traits5–7. However, our understanding of the function and regulation of leader cells during collective cancer invasion remains limited3,8.  \nThree-dimensional (3D) invasion assays with time-lapse, multimodal imaging modalities provide effective approaches to study the formation and regulation of leader cells in microengineered tumor environments9–14. Protruding structures with leader-follower organization from tumor spheroids exhibit diverse phenotypic behaviors during cancer dissemination. The mode of collective cancer invasion, such as budding, sprouting, multicellular streaming, and detached clusters, can be classified depending on the dimensions (e.g., volume, length, and aspect ratio) of the invading structures. By incorporating fluorescent nanobiosensors13, 15, molecular signatures of leader cells can also be investigated, shedding light on the spatiotemporal regulation of cancer invasion. For example, several genes, including p53, Dll4, and MALAT1, are upregulated in leader cells associated with epithelial-mesenchymal transition14, 16–18. Basal epithelial cell gene signatures, including cytokeratin- 14 and p63, are also reported in leader cells without common markers of epithelial-mesenchymal transition19. Furthermore, MYO10, RAB13 RNA, and NET1 RNA are accumulated at the invasive front of leader cells to support path generation by leader cells12,20. These studies underscore that leader cells are associated with diverse molecular mechanisms and phenotypes3.  \nElucidating the heterogeneity of cancer invasion requires the analysis of a large number of invasion events. Th","cbCaiheah7jOVI1N","https://ap.wps.com/l/cbCaiheah7jOVI1N","pdf",3252702,11,"English","# Background\n## Collective cancer invasion and leader cells\n## 3D invasion assays and nanobiosensor-based signatures\n# Need for automated analysis\n## Limitations of manual quantification\n# Proposed workflow\n## Computer vision-based 3D image processing\n## Segmentation, feature extraction, and gene-expression readouts\n# Application\n## MALAT1 heterogeneity analysis in tumor spheroids","[{\"question\":\"What problem does the proposed workflow address?\",\"answer\":\"It addresses the technological challenge of analyzing time-lapse, multimodal 3D imaging and gene-expression profiles, which currently often requires time-consuming manual quantification.\"},{\"question\":\"How does the workflow identify invading structures in 3D tumor spheroids?\",\"answer\":\"It uses a computer vision-based 3D image processing workflow that performs denoising and segmentation of 3D confocal images, then identifies protrusions and detached clusters from the segmented volumes.\"},{\"question\":\"What biological insight is demonstrated using MALAT1?\",\"answer\":\"The approach is applied to patient-derived tumor spheroids to investigate invasion heterogeneity and how it relates to MALAT1 expression.\"}]","Three-dimensional computer vision for exploring heterogeneity in collective Cancer Invasion - Abstract | PDF",1789998839,28]