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Cervilla, S.

Publications and source records attributed to Cervilla, S..

5 recordsLinked to original sources

RNA and protein are not interchangeable in single-cell spatial multi-omics

Spatial transcriptomics is increasingly used to infer cellular phenotypes, molecular interactions, and functional tissue organization from RNA, although most of these biological processes are ultimately mediated by proteins. Here, we directly tested the interchangeability of spatial RNA and protein measurements using paired Xenium profiling of 21 matched targets across ~1.8 million cells from primary lung adenocarcinoma and metastatic castration-resistant prostate cancer. After accounting for marker- and dataset-specific protein background, we quantified concordance at multiple levels, including single-cell detection, expression magnitude, cell-type distribution, patient-level abundance, and spatial organization. Agreement at single-cell level was strongly marker-dependent. Canonical lineage markers generally showed concordant RNA and protein patterns, whereas several molecules, including in our study PD-1, PD-L1, and LAG-3, showed little concordance across modalities. Importantly, spatial organization remained substantially more discordant between modalities than overall abundance, indicating that spatial biomarkers are not directly transferable across modalities. Together, our results show that spatial RNA and protein measurements provide largely overlapping information for lineage markers and overall abundance, but can diverse for specific molecules and for higher-order spatial features, where the two layers provide complementary rather than equivalent readouts. These findings provide practical guidance on when spatial transcriptomic readouts can be interpreted as proxies for protein and when orthogonal protein validation is advisable.

cancer biology↗

A pan-cancer single-cell atlas of pericytes

Pericytes display marked tissue-specific transcriptional identities, raising the question of whether tumour-associated pericytes converge towards shared adaptive states across cancers. Here, we built a pan-cancer single-cell RNA sequencing atlas of pericytes, integrating nearly four million cells across nine tissues, complemented by spatial transcriptomic analyses. Despite their physiological diversity, pericytes were recurrently expanded in tumours and converged on a common transcriptional program, the tumour-associated pericyte signature (TAPS). TAPS robustly identified pericytes across datasets, outperforming canonical markers in tumour contexts. Tumour-associated pericytes further diversified into specialised states including extracellular matrix (ECM)-associated and interferon (IFN)-responsive programs, which occupy mutually exclusive tumour ecosystems. ECM-associated pericytes were enriched in desmoplastic, fibroblast-rich regions and were associated with adverse clinical outcomes across multiple cancer types. IFN-responsive pericytes accumulated in inflammatory niches, with macrophages implicated as candidate drivers of specialisation. Together, our multi-layered analysis defines convergent and specialised tumour-associated pericyte programs across human cancers.

cancer biology↗

Convergent stromal and immune remodeling defines spatial tumor dynamics in PARP inhibitor-resistant high-grade serous ovarian cancer

Acquired resistance to poly(ADP-ribose) polymerase inhibitors (PARPi) remains a major barrier to durable clinical benefit in patients with high-grade serous ovarian cancer (HGSOC), yet the profiles of tumors at resistance remain poorly understood. We profiled longitudinal HGSOC patient tumors spanning diagnosis, post-neoadjuvant therapy, and progression on PARPi with complementary spatial transcriptomics platforms, integrating single-cell-resolution Xenium analysis in paired longitudinal cases with cohort-level Visium profiling. PARPi failure was associated with spatial remodeling of the tumor microenvironment, marked by hypoxia-associated malignant transcriptional programs, increased stromal compartmentalization, and exclusion of effector immune cells. Together, these findings indicate that PARPi resistance in HGSOC is accompanied by reproducible spatial niche remodeling, underscoring the tumor microenvironment as a key contextual determinant of therapeutic failure.

cancer biology↗

Comparison of spatial transcriptomics technologies across six cancer types

Spatial biology experiments integrate the molecular and histological landscape of tissues to provide a previously inaccessible view of tissue biology, unlocking the architecture of complex multicellular tissues. Within spatial biology, spatial transcriptomics platforms are among the most advanced, allowing researchers to characterize the expression of thousands of genes across space. These new technologies are transforming our understanding of how cells are organized in space and communicate with each other to determine emergent phenotypes. This is particularly important in cancer research, as tumor evolution is shaped not only by the genetic properties of cancer cells but also by how they interact with the tumor microenvironment and their spatial organization. While many platforms can generate spatial transcriptomics profiles, it is still unclear in which context each platform better suits the needs of its users. Here we compare the results obtained using 5 different spatial transcriptomics (VISIUM, VISIUM CytAssist, VisiumHD, Xenium, and CosMx) and one spatial proteomics (VISIUM CytAssist) platforms across serial sections of 6 FFPE samples from primary human tumors covering some of the most common forms of the disease (lung, breast, colorectal, bladder, lymphoma and ovary). We observed that the VISIUM platform with CytAssist chemistry yielded superior data quality than manual VISIUM. On the other hand, Xenium consistently produced more reliable results for in situ platforms, with better gene clustering and fewer false positives than CosMx. Importantly, these platform-based variations didnt significantly affect cell type identification. VisiumHD offers the best options of both worlds, with subcellular resolution and whole-transcriptome coverage, albeit with some limitations that need to be accounted for in downstream analyses. Finally, by comparing VISIUM protein profiles with the spatial transcriptomics data from all four platforms on each sample, we identified several genes with mismatched RNA and protein expression patterns, highlighting the importance of multi-omics profiling to reveal the true biology of human tumors.

genomics↗

Charting the Spatial Landscape of Cancer Hallmarks

Tumors are complex ecosystems with dozens of interacting cell types. The concept of Cancer Hallmarks distills this complexity into a set of underlying principles that govern tumor growth. Here, we exploit this abstraction to explore the physical distribution of Cancer Hallmarks across 63 primary untreated tumors from 10 cancer types using spatial transcriptomics. We show that Hallmark activity is spatially organized-with 7 out of 13 Hallmarks consistently more active in cancer cells than within the non-cancerous tumor microenvironment (TME). The opposite is true for the remaining six Hallmarks. Additionally, we discovered that genomic distance between tumor subclones correlates with differences in Cancer Hallmark activity, even leading to clone-Hallmark specialization in some cases. Finally, we demonstrate interdependent relationships between Cancer Hallmarks at the junctions of TME and cancer compartments. In conclusion, including the spatial dimension, particularly through the lens of Cancer Hallmarks, can improve our understanding of tumor ecology. SignificanceWe explored Cancer Hallmarks in 63 primary untreated tumors from 10 cancer types using spatial transcriptomics. This study unveiled spatial patterns in Hallmark activity, with some being more active in cancer cells and others in the non-cancerous tumor environment. Genomic distance impacted Hallmark activity, and we identified interdependencies at the TME-cancer junctions, improving our understanding of tumor ecology.

cancer biology↗