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Single-cell RNA sequencing (scRNA-seq) identifies cell subpopulations within tissue but does not capture their spatial distribution nor reveal local networks of intercellular communication acting in situ. A suite of recently developed techniques that localize RNA within tissue, including multiplexed in situ hybridization and in situ sequencing (here defined as high-plex RNA imaging) and spatial barcoding, can help address this issue. However, no method currently provides as complete a scope of the transcriptome as does scRNA-seq, underscoring the need for approaches to integrate single-cell and spatial data. Here, we review efforts to integrate scRNA-seq with spatial transcriptomics, including emerging integrative computational methods, and propose ways to effectively combine current methodologies.Combining single-cell RNA sequencing (scRNA-seq) and spatial transcriptomics can localize transcriptionally characterized single cells within their native tissue context. This Review discusses methodologies and tools to integrate scRNA-seq with spatial transcriptomics approaches, and illustrates the types of insights that can be gained.

Sarcoidosis is an inflammatory disease characterized by immune cell-rich granulomas that form in multiple organs. In this issue of the JCI, Sati and colleagues used scRNA-seq and spatial transcriptomics of skin samples from patients with sarcoidosis and non-sarcoidosis granulomatous disease to identify upregulation of a stromal-immune CXCL12/CXCR4 axis and accumulation of type 1 innate lymphoid cells (ILC1s) in sarcoidosis. The accumulation of ILC1s in skin and blood was specific to patients with sarcoidosis and not observed in other granulomatous diseases. The authors used a mouse model of lung granuloma to show that ILCs contribute to granuloma formation and that blockade of CXCR4 reduced the formation of granulomas, providing a proof of concept that sarcoidosis may be treated by CXCR4 blockade to prevent the progression of disease in patients. These results suggest ILC1s could serve as a diagnostic biomarker in sarcoidosis and a potential therapeutic target.

Quantitative criteria to identify proteins as RNA-binding proteins (RBPs) are presently lacking, as are criteria to define RBP target RNAs. Here, we develop an ultraviolet (UV) cross-linking immunoprecipitation (CLIP)-sequencing method, easyCLIP. easyCLIP provides absolute cross-link rates, as well as increased simplicity, efficiency, and capacity to visualize RNA libraries during sequencing library preparation. Measurement of >200 independent cross-link experiments across >35 proteins identifies an RNA cross-link rate threshold that distinguishes RBPs from non-RBPs and defines target RNAs as those with a complex frequency unlikely for a random protein. We apply easyCLIP to the 33 most recurrent cancer mutations across 28 RBPs, finding increased RNA binding per RBP molecule for KHDRBS2 R168C, A1CF E34K and PCBP1 L100P/Q cancer mutations. Quantitating RBP-RNA interactions can thus nominate proteins as RBPs and define the impact of specific disease-associated RBP mutations on RNA association. It is important to develop quantitative criteria to identify RNA-binding proteins and target RNAs. Here, the authors present a method to quantify cross-linking and apply it to recurrent cancer mutations in RNA-binding proteins.

Spatial transcriptomic technologies, such as the Visium platform, measure gene expression in different regions of tissues. Here, we describe new software, STmut, to visualize somatic point mutations, allelic imbalance, and copy number alterations in Visium data. STmut is tested on fresh-frozen Visium data, formalin-fixed paraffin-embedded (FFPE) Visium data, and tumors with and without matching DNA sequencing data. Copy number is inferred on all conditions, but the chemistry of the FFPE platform does not permit analyses of single nucleotide variants. Taken together, we propose solutions to add the genetic dimension to spatial transcriptomic data and describe the limitations of different datatypes.

Current methods for spatial transcriptomics are limited by low spatial resolution. Here we introduce a method that integrates spatial gene expression data with histological image data from the same tissue section to infer higher-resolution expression maps. Using a deep generative model, our method characterizes the transcriptome of micrometer-scale anatomical features and can predict spatial gene expression from histology images alone. The low resolution of spatial transcriptomics is substantially improved by including histology images.

Defining the transition from benign to malignant tissue is fundamental to improving early diagnosis of cancer 1 . Here we use a systematic approach to study spatial genome integrity in situ and describe previously unidentified clonal relationships. We used spatially resolved transcriptomics 2 to infer spatial copy number variations in >120,000 regions across multiple organs, in benign and malignant tissues. We demonstrate that genome-wide copy number variation reveals distinct clonal patterns within tumours and in nearby benign tissue using an organ-wide approach focused on the prostate. Our results suggest a model for how genomic instability arises in histologically benign tissue that may represent early events in cancer evolution. We highlight the power of capturing the molecular and spatial continuums in a tissue context and challenge the rationale for treatment paradigms, including focal therapy. Copy number variations inferred from spatial transcriptomics data in benign and malignant tissue reveal clonal architecture at the organ-wide level.

Tumour cells respond to an effective, targeted drug treatment with BRAF, ALK or EGFR kinase inhibitors by inducing a complex network of secreted signals that promote tumour growth, dissemination and metastasis of drug-resistant cancer cell clones, and increase the survival of drug-sensitive tumour cells, potentially contributing to incomplete tumour regression. Tumour progression due to targeted therapy Targeted kinase inhibitors such as have proved clinically effective in melanoma with BRAF mutations and in lung adenocarcinoma with EGFR mutations or ALK translocations, but drug resistance almost always follows. This study by Joan Massagué and colleagues shows that tumour cells responding to kinase inhibitors induce a complex network of secreted signals that promote tumour growth, dissemination and metastasis of drug-resistant cancer cell clones, and increase the survival of drug-sensitive tumour cells, potentially contributing to incomplete tumour regression. Drug resistance invariably limits the clinical efficacy of targeted therapy with kinase inhibitors against cancer 1 , 2 . Here we show that targeted therapy with BRAF, ALK or EGFR kinase inhibitors induces a complex network of secreted signals in drug-stressed human and mouse melanoma and human lung adenocarcinoma cells. This therapy-induced secretome stimulates the outgrowth, dissemination and metastasis of drug-resistant cancer cell clones and supports the survival of drug-sensitive cancer cells, contributing to incomplete tumour regression. The tumour-promoting secretome of melanoma cells treated with the kinase inhibitor vemurafenib is driven by downregulation of the transcription factor FRA1. In situ transcriptome analysis of drug-resistant melanoma cells responding to the regressing tumour microenvironment revealed hyperactivation of several signalling pathways, most prominently the AKT pathway. Dual inhibition of RAF and the PI(3)K/AKT/mTOR intracellular signalling pathways blunted the outgrowth of the drug-resistant cell population in BRAF mutant human melanoma, suggesting this combination therapy as a strategy against tumour relapse. Thus, therapeutic inhibition of oncogenic drivers induces vast secretome changes in drug-sensitive cancer cells, paradoxically establishing a tumour microenvironment that supports the expansion of drug-resistant clones, but is susceptible to combination therapy.

Objective. To examine trends in melanoma visits in the ambulatory care setting. Methods. Data from the National Ambulatory Medical Care Survey (NAMCS) from 1979 to 2010 were used to analyze melanoma visit characteristics including number of visits, age and gender of patients, and physician specialty. These data were compared to US Census population estimates during the same time period. Results. The overall rate of melanoma visits increased (P<0.0001) at an apparently higher rate than the increase in population over this time. The age of patients with melanoma visits increased at approximately double the rate (0.47 year per interval year, P<0.0001) of the population increase in age (0.23 year per interval year). There was a nonsignificant (P=0.19) decline in the proportion of female patients seen over the study interval. Lastly, ambulatory care has shifted towards dermatologists and other specialties managing melanoma patients and away from family/internal medicine physicians and general/plastic surgeons. Conclusions. The number and age of melanoma visits has increased over time with respect to the overall population, mirroring the increase in melanoma incidence over the past three decades. These trends highlight the need for further studies regarding melanoma management efficiency.