Recently developed spatial gene expression technologies such as the SpatialTranscriptomics and Visium platforms allow for comprehensive measurement of transcriptomic profiles while retaining spatial context. However, existing methods for analyzing spatial gene expression data often do not efficiently leverage the spatial information and fail to address the limited resolution of the technology. Here, we introduce BayesSpace, a fully Bayesian statistical method for clustering analysis and resolution enhancement of spatial transcriptomics data that seamlessly integrates into current transcriptomics analysis workflows. We show that BayesSpace improves the identification of transcriptionally distinct tissues from spatial transcriptomics samples of the brain, of melanoma, and of squamous cell carcinoma. In particular, BayesSpace's improved resolution allows the identification of tissue structure that is not detectable at the original resolution and thus not recovered by other methods. Using an in silico dataset constructed from scRNA-seq, we demonstrate that BayesSpace can spatially resolve expression patterns to near single-cell resolution without the need for external single-cell sequencing data.In all, our results illustrate the utility BayesSpace has in facilitating the discovery of biological insights from a variety of spatial transcriptomics datasets.

The cellular adaptive immune system mounts a response to many solid tumors mediated by tumor infiltrating T lymphocytes (TILs). Basic measurements of these TILs, including total count, show promise as prognostic markers for a variety of cancers, including ovarian and colorectal. In addition, recent therapeutic advances are thought to exploit this immune response to effectively fight melanoma with promising studies showing efficacy in additional cancers. However, many of the basic properties of TILs are poorly understood including specificity, clonality, and spatial heterogeneity of the T cell response. We utilize deep sequencing of rearranged T-cell receptor beta (TCRB) genes to characterize the basic properties of TILs in ovarian carcinoma. Due to somatic rearrangement during T cell development, the TCR beta chain sequence serves as a molecular tag for each T cell clone. Using these sequence tags, we assess similarities and differences between infiltrating T cells in discretely sampled sections of large tumors and compare to T cells from peripheral blood. Within the limits of sensitivity of our assay, the TIL repertoires show strong similarity throughout each tumor and are distinct from the circulating T cell repertoire. We conclude that the cellular adaptive immune response within ovarian carcinomas is spatially homogeneous and distinct from the T cell compartment of peripheral blood.

Purpose Residual disease (RD) following primary cytoreduction is associated with adverse overall survival in patients with epithelial ovarian cancer. Accurate identification of patients at high risk of RD has been elusive, lacking external validity and prompting many to undergo unnecessary surgical exploration. Our goal was to identify and validate molecular markers associated with high rates of residual disease. Methods We interrogated two publicly available datasets from chemonaïve primary high-grade serous ovarian tumors for genes overexpressed in patients with RD and significant at a 10% false discovery rate (FDR) in both datasets. We selected genes with wide dynamic range for validation in an independent cohort using qRT-PCR to assay gene expression, followed by blinded prediction of a patient subset at high risk for RD. Predictive success was evaluated using a one-sided Fisher’s exact test. Results Forty-seven probesets met the 10% FDR criterion in both datasets. These included FABP4 and ADH1B, which tracked tightly, showed dynamic ranges >16-fold, and had high expression levels associated with increased incidence of RD. In the validation cohort (n=189), FABP4 and ADH1B were again highly correlated. Using the top quartile of FABP4 PCR values as a pre-specified threshold, we found 30/35 cases of RD in the predicted high-risk group (positive predictive value 86%), and 54/104 among the remaining patients (P=0.0002; odds ratio 5.5). Conclusion High FABP4 and ADH1B expression are associated with significantly higher risk of residual disease in high-grade serous ovarian cancer. Patients with high tumoral levels of these genes may be candidates for neoadjuvant chemotherapy.