High-plex imaging of RNA and proteins at subcellular resolution in fixed tissue by spatial molecular imaging.

Resolving the spatial distribution of RNA and protein in tissues at subcellular resolution is a challenge in the field of spatial biology. We describe spatial molecular imaging, a system that measures RNAs and proteins in intact biological samples at subcellular resolution by performing multiple cycles of nucleic acid hybridization of fluorescent molecular barcodes. We demonstrate that spatial molecular imaging has high sensitivity (one or two copies per cell) and very low error rate (0.0092 false calls per cell) and background (~0.04 counts per cell). The imaging system generates three-dimensional, super-resolution localization of analytes at ~2 million cells per sample. Cell segmentation is morphology based using antibodies, compatible with formalin-fixed, paraffin-embedded samples. We measured multiomic data (980 RNAs and 108 proteins) at subcellular resolution in formalin-fixed, paraffin-embedded tissues (nonsmall cell lung and breast cancer) and identified >18 distinct cell types, ten unique tumor microenvironments and 100 pairwise ligand-receptor interactions. Data on >800,000 single cells and ~260 million transcripts can be accessed at http://nanostring.com/CosMx-dataset .

Spatially resolved whole transcriptome profiling in human and mouse tissue using Digital Spatial Profiling.

Emerging spatial profiling technology has enabled high-plex molecular profiling in biological tissues, preserving the spatial and morphological context of gene expression. Here, we describe expanding the chemistry for the Digital Spatial Profiling platform to quantify whole transcriptomes in human and mouse tissues using a wide range of spatial profiling strategies and sample types. We designed multiplexed in situ hybridization probes targeting the protein-coding genes of the human and mouse transcriptomes, referred to as the human or mouse Whole Transcriptome Atlas (WTA). Human and mouse WTAs were validated in cell lines for concordance with orthogonal gene expression profiling methods in regions ranging from ∼10-500 cells. By benchmarking against bulk RNA-seq and fluorescence in situ hybridization, we show robust transcript detection down to ∼100 transcripts per region. To assess the performance of WTA across tissue and sample types, we applied WTA to biological questions in cancer, molecular pathology, and developmental biology. Spatial profiling with WTA detected expected gene expression differences between tumor and tumor microenvironment, identified disease-specific gene expression heterogeneity in histological structures of the human kidney, and comprehensively mapped transcriptional programs in anatomical substructures of nine organs in the developing mouse embryo. Digital Spatial Profiling technology with the WTA assays provides a flexible method for spatial whole transcriptome profiling applicable to diverse tissue types and biological contexts.

Advances in mixed cell deconvolution enable quantification of cell types in spatial transcriptomic data.

Mapping cell types across a tissue is a central concern of spatial biology, but cell type abundance is difficult to extract from spatial gene expression data. We introduce SpatialDecon, an algorithm for quantifying cell populations defined by single cell sequencing within the regions of spatial gene expression studies. SpatialDecon incorporates several advancements in gene expression deconvolution. We propose an algorithm harnessing log-normal regression and modelling background, outperforming classical least-squares methods. We compile cell profile matrices for 75 tissue types. We identify genes whose minimal expression by cancer cells makes them suitable for immune deconvolution in tumors. Using lung tumors, we create a dataset for benchmarking deconvolution methods against marker proteins. SpatialDecon is a simple and flexible tool for mapping cell types in spatial gene expression studies. It obtains cell abundance estimates that are spatially resolved, granular, and paired with highly multiplexed gene expression data.

CTLA-4 blockade and interferon-α induce proinflammatory transcriptional changes in the tumor immune landscape that correlate with pathologic response in melanoma.

Patients with locally/regionally advanced melanoma were treated with neoadjuvant combination immunotherapy with high-dose interferon α-2b (HDI) and ipilimumab in a phase I clinical trial. Tumor specimens were obtained prior to the initiation of neoadjuvant therapy, at the time of surgery and progression if available. In this study, gene expression profiles of tumor specimens (N = 27) were investigated using the NanoString nCounter® platform to evaluate associations with clinical outcomes (pathologic response, radiologic response, relapse-free survival (RFS), and overall survival (OS)) and define biomarkers associated with tumor response. The Tumor Inflammation Signature (TIS), an 18-gene signature that enriches for response to Programmed cell death protein 1 (PD-1) checkpoint blockade, was also evaluated for association with clinical response and survival. It was observed that neoadjuvant ipilimumab-HDI therapy demonstrated an upregulation of immune-related genes, chemokines, and transcription regulator genes involved in immune cell activation, function, or cell proliferation. Importantly, increased expression of baseline pro-inflammatory genes CCL19, CD3D, CD8A, CD22, LY9, IL12RB1, C1S, C7, AMICA1, TIAM1, TIGIT, THY1 was associated with longer OS (p < 0.05). In addition, multiple genes that encode a component or a regulator of the extracellular matrix such as MMP2 and COL1A2 were identified post-treatment as being associated with longer RFS and OS. In all baseline tissues, high TIS scores were associated with longer OS (p = 0.0166). Also, downregulated expression of cell proliferation-related genes such as CUL1, CCND1 and AAMP at baseline was associated with pathological and radiological response (unadjusted p < 0.01). In conclusion, we identified numerous genes that play roles in multiple biological pathways involved in immune activation, immune suppression and cell proliferation correlating with pathological/radiological responses following neoadjuvant immunotherapy highlighting the complexity of immune responses modulated by immunotherapy. Our observations suggest that TIS may be a useful biomarker for predicting survival outcomes with combination immunotherapy.

A novel systems biology approach to evaluate mouse models of late-onset Alzheimer's disease.

BACKGROUND: Late-onset Alzheimer's disease (LOAD) is the most common form of dementia worldwide. To date, animal models of Alzheimer's have focused on rare familial mutations, due to a lack of frank neuropathology from models based on common disease genes. Recent multi-cohort studies of postmortem human brain transcriptomes have identified a set of 30 gene co-expression modules associated with LOAD, providing a molecular catalog of relevant endophenotypes. RESULTS: This resource enables precise gene-based alignment between new animal models and human molecular signatures of disease. Here, we describe a new resource to efficiently screen mouse models for LOAD relevance. A new NanoString nCounter® Mouse AD panel was designed to correlate key human disease processes and pathways with mRNA from mouse brains. Analysis of the 5xFAD mouse, a widely used amyloid pathology model, and three mouse models based on LOAD genetics carrying APOE4 and TREM2*R47H alleles demonstrated overlaps with distinct human AD modules that, in turn, were functionally enriched in key disease-associated pathways. Comprehensive comparison with full transcriptome data from same-sample RNA-Seq showed strong correlation between gene expression changes independent of experimental platform. CONCLUSIONS: Taken together, we show that the nCounter Mouse AD panel offers a rapid, cost-effective and highly reproducible approach to assess disease relevance of potential LOAD mouse models.