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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. The deconvolution of cell types is challenging in spatially-resolved transcriptomics. Here, the authors present SpatialDecon, a method for the deconvolution and quantification of cell types in spatial transcriptomics data, and show how it can be used to analyse immune response heterogeneity in cancer.

Spatial transcriptomics (ST) faces persistent challenges in cell segmentation accuracy, which can bias biological interpretations in a spatial-dependent way. FastReseg introduces a novel algorithm that refines inaccuracies in existing image-based segmentations using transcriptomic data, without radically redefining cell boundaries. By combining image-based information with 3D transcriptomic precision, FastReseg enhances segmentation accuracy. Its key innovation, a transcript scoring system based on log-likelihood ratios, facilitates the quick identification and correction of spatial doublets caused by cell proximity or overlap in 2D. FastReseg reduces circularity in boundary derivation, and addresses computational challenges with a modular workflow designed for large datasets. The algorithm’s modularity allows for seamless optimization and integration of advancements in segmentation technology. FastReseg provides a scalable, efficient solution to improve the quality and interpretability of ST data, ensuring compatibility with evolving segmentation methods and enabling more accurate biological insights.

In spatial transcriptomics data, spatially correlated genes promise to reveal high-interest phenomena like cell–cell interactions and latent variables. But in practice, most spatial correlations arise from the spatial arrangement of cell types, obscuring the more interesting relationships we hope to discover. We introduce InSituCor, a toolkit for discovering modules of spatially correlated genes. InSituCor returns only correlations not explainable by already-known factors like the cell type landscape; this spares precious analyst effort. InSituCor supports both unbiased discovery of whole-dataset correlations and knowledge-driven exploration of genes of interest. As a special case, it evaluates ligand-receptor pairs for spatial co-regulation.

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.

Digital Spatial Profiling (DSP) is a method for highly multiplex spatial profiling of proteins or RNAs suitable for use on formalin-fixed, paraffin-embedded (FFPE) samples. The approach relies on (1) multiplexed readout of proteins or RNAs using oligonucleotide tags; (2) oligonucleotide tags attached to affinity reagents (antibodies or RNA probes) through a photocleavable (PC) linker; and (3) photocleaving light projected onto the tissue sample to release PC oligonucleotides in any spatial pattern across a region of interest (ROI) covering 1 to ~5,000 cells. DSP is capable of single-cell sensitivity within an ROI using the antibody readout, with RNA detection feasible down to ~600 individual mRNA transcripts. We show spatial profiling of up to 44 proteins and 96 genes (928 RNA probes) in lymphoid, colorectal tumor and autoimmune tissues by using the nCounter system and 1,412 genes (4,998 RNA probes) by using next-generation sequencing (NGS). DSP may be used to profile not only proteins and RNAs in biobanked samples but also immune markers in patient samples, with potential prognostic and predictive potential for clinical decision-making. A turnkey system allows for spatial profiling of proteins and RNA in fixed tissues, providing a window on cellular heterogeneity.

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 . Hundreds of RNAs and proteins are imaged in fixed tissue at subcellular resolution.

Head and neck squamous cell carcinoma (HNSCC) is a debilitating disease that accounts for an estimated 890,000 new cases per year. Despite advancements in chemotherapy, radiotherapy, surgery and immunotherapy, the prognosis of HNSCC has remained relatively unchanged for more than a decade. Insight into the tumour microenvironment (TME) using spatially resolved approaches, and its association with clinical endpoints, may provide useful prognostic tools and refine current treatment outcomes. Here, we profiled 84 mucosal HNSCC tissue samples using next-generation ultra-high plex spatial protein profiling (580-proteins, Immuno-Oncology Proteome Atlas (IPA)) and spatial transcriptome mapping (18,000 mRNA, Whole Transcriptome Atlas (WTA)) from Bruker Spatial Biology. Samples were collected during tumour resection, after which patients went on to receive either chemotherapy and/or radiotherapy. Each sample was subdivided into tumour and stromal regions prior to digital spatial profiling. We found that patient survival outcomes were associated with anatomical subsite and tumour stage. Independent validation of key proteomic findings (including CD34 and CD44) was performed using single-cell protein profiling (PhenoCycler-Fusion, Akoya Biosciences). Harnessing the breadth of the 580-plex protein panel and WTA, we identified region-specific proteins and RNA that associate with patient survival. These findings include the expression of immune-specific proteins, CD3e and CXCR5, differentially expressed in the tumour compartment and indicate that the location of these immune signals is important for understanding disease progression. Taken together, this study provides a systematic workflow for the discovery and validation of high-plex protein and transcriptomic profiling in mucosal HNSCC.

In combination with cell-intrinsic properties, interactions in the tumor microenvironment modulate therapeutic response. We leveraged single-cell spatial transcriptomics to dissect the remodeling of multicellular neighborhoods and cell–cell interactions in human pancreatic cancer associated with neoadjuvant chemotherapy and radiotherapy. We developed spatially constrained optimal transport interaction analysis (SCOTIA), an optimal transport model with a cost function that includes both spatial distance and ligand–receptor gene expression. Our results uncovered a marked change in ligand–receptor interactions between cancer-associated fibroblasts and malignant cells in response to treatment, which was supported by orthogonal datasets, including an ex vivo tumoroid coculture system. We identified enrichment in interleukin-6 family signaling that functionally confers resistance to chemotherapy. Overall, this study demonstrates that characterization of the tumor microenvironment using single-cell spatial transcriptomics allows for the identification of molecular interactions that may play a role in the emergence of therapeutic resistance and offers a spatially based analysis framework that can be broadly applied to other contexts. Spatial molecular imaging analysis of human pancreatic adenocarcinomas describes multicellular neighborhoods in the tumor microenvironment. Ligand–receptor analysis using optimal transport-based SCOTIA identifies interleukin-6 as a mediator of chemoresistance.

The chaperonin GroEL is a double-ring structure with a central cavity in each ring that provides an environment for the efficient folding of proteins when capped by the co-chaperone GroES in the presence of adenine nucleotides. Productive folding of the substrate rhodanese has been observed in cis ternary complexes, where GroES and polypeptide are bound to the same ring, formed with either ATP, ADP or non-hydrolysable ATP analogues, suggesting that the specific requirement for ATP is confined to an action in the trans ring that evicts GroES and polypeptide from the cis side. We show here, however, that for the folding of malate dehydrogenase and Rubisco there is also an absolute requirement for ATP in the cis ring, as ADP and AMP-PNP are unable to promote folding. We investigated the specific roles of binding and hydrolysis of ATP in the cis and trans rings using mutant forms of GroEL that bind ATP but are defective in its hydrolysis. Binding of ATP and GroES in cis initiated productive folding inside a highly stable GroEL-ATP-GroES complex. To discharge GroES and polypeptide, ATP hydrolysis in the cis ring was required to form a GroEL-ADP-GroES complex with decreased stability, priming the cis complex for release by ATP binding (without hydrolysis) in the trans ring. These observations offer an explanation of why GroEL functions as a double-ring complex.

Patients with locally/regionally advanced melanoma were treated with neoadjuvant combination immunotherapy with high-dose interferon [alpha]-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.