SpatialCells: automated profiling of tumor microenvironments with spatially resolved multiplexed single-cell data.

Cancer is a complex cellular ecosystem where malignant cells coexist and interact with immune, stromal and other cells within the tumor microenvironment (TME). Recent technological advancements in spatially resolved multiplexed imaging at single-cell resolution have led to the generation of large-scale and high-dimensional datasets from biological specimens. This underscores the necessity for automated methodologies that can effectively characterize molecular, cellular and spatial properties of TMEs for various malignancies. This study introduces SpatialCells, an open-source software package designed for region-based exploratory analysis and comprehensive characterization of TMEs using multiplexed single-cell data. The source code and tutorials are available at https://semenovlab.github.io/SpatialCells. SpatialCells efficiently streamlines the automated extraction of features from multiplexed single-cell data and can process samples containing millions of cells. Thus, SpatialCells facilitates subsequent association analyses and machine learning predictions, making it an essential tool in advancing our understanding of tumor growth, invasion and metastasis.

Multiplexed 3D Analysis of Immune States and Niches in Human Tissue.

Tissue homeostasis and the emergence of disease are controlled by changes in the proportions of resident and recruited cells, their organization into cellular neighbourhoods, and their interactions with acellular tissue components. Highly multiplexed tissue profiling (spatial omics) 1 makes it possible to study this microenvironment in situ , usually in 4-5 micron thick sections (the standard histopathology format) 2 . Microscopy-based tissue profiling is commonly performed at a resolution sufficient to determine cell types but not to detect subtle morphological features associated with cytoskeletal reorganisation, juxtracrine signalling, or membrane trafficking 3 . Here we describe a high-resolution 3D imaging approach able to characterize a wide variety of organelles and structures at sub-micron scale while simultaneously quantifying millimetre-scale spatial features. This approach combines cyclic immunofluorescence (CyCIF) imaging 4 of over 50 markers with confocal microscopy of archival human tissue thick enough (30-40 microns) to fully encompass two or more layers of intact cells. 3D imaging of entire cell volumes substantially improves the accuracy of cell phenotyping and allows cell proximity to be scored using plasma membrane apposition, not just nuclear position. In pre-invasive melanoma in situ 5 , precise phenotyping shows that adjacent melanocytic cells are plastic in state and participate in tightly localised niches of interferon signalling near sites of initial invasion into the underlying dermis. In this and metastatic melanoma, mature and precursor T cells engage in an unexpectedly diverse array of juxtracrine and membrane-membrane interactions as well as looser "neighbourhood" associations 6 whose morphologies reveal functional states. These data provide new insight into the transitions occurring during early tumour formation and immunoediting and demonstrate the potential for phenotyping of tissues at a level of detail previously restricted to cultured cells and organoids.

Development and validation of time-to-event models to predict metastatic recurrence of localized cutaneous melanoma.

BACKGROUND: The recent expansion of immunotherapy for stage IIB/IIC melanoma highlights a growing clinical need to identify patients at high risk of metastatic recurrence and, therefore, most likely to benefit from this therapeutic modality. OBJECTIVE: To develop time-to-event risk prediction models for melanoma metastatic recurrence. METHODS: Patients diagnosed with stage I/II primary cutaneous melanoma between 2000 and 2020 at Mass General Brigham and Dana-Farber Cancer Institute were included. Melanoma recurrence date and type were determined by chart review. Thirty clinicopathologic factors were extracted from electronic health records. Three types of time-to-event machine-learning models were evaluated internally and externally in the distant versus locoregional/nonrecurrence prediction. RESULTS: This study included 954 melanomas (155 distant, 163 locoregional, and 636 1:2 matched nonrecurrences). Distant recurrences were associated with worse survival compared to locoregional/nonrecurrences (HR: 6.21, P < .001) and to locoregional recurrences only (HR: 5.79, P < .001). The Gradient Boosting Survival model achieved the best performance (concordance index: 0.816; time-dependent AUC: 0.842; Brier score: 0.103) in the external validation. LIMITATIONS: Retrospective nature and cohort from one geography. CONCLUSIONS: These results suggest that time-to-event machine-learning models can reliably predict the metastatic recurrence from localized melanoma and help identify high-risk patients who are most likely to benefit from immunotherapy.

SpatialCells: Automated Profiling of Tumor Microenvironments with Spatially Resolved Multiplexed Single-Cell Data.

Background: Cancer is a complex cellular ecosystem where malignant cells coexist and interact with immune, stromal, and other cells within the tumor microenvironment. Recent technological advancements in spatially resolved multiplexed imaging at single-cell resolution have led to the generation of large-scale and high-dimensional datasets from biological specimens. This underscores the necessity for automated methodologies that can effectively characterize the molecular, cellular, and spatial properties of tumor microenvironments for various malignancies. Results: This study introduces SpatialCells, an open-source software package designed for region-based exploratory analysis and comprehensive characterization of tumor microenvironments using multiplexed single-cell data. Conclusions: SpatialCells efficiently streamlines the automated extraction of features from multiplexed single-cell data and can process samples containing millions of cells. Thus, SpatialCells facilitates subsequent association analyses and machine learning predictions, making it an essential tool in advancing our understanding of tumor growth, invasion, and metastasis.

Spatial intra-tumor heterogeneity is associated with survival of lung adenocarcinoma patients.

Intra-tumor heterogeneity (ITH) of human tumors is important for tumor progression, treatment response, and drug resistance. However, the spatial distribution of ITH remains incompletely understood. Here, we present spatial analysis of ITH in lung adenocarcinomas from 147 patients using multi-region mass spectrometry of >5,000 regions, single-cell copy number sequencing of ~2,000 single cells, and cyclic immunofluorescence of >10 million cells. We identified two distinct spatial patterns among tumors, termed clustered and random geographic diversification (GD). These patterns were observed in the same samples using both proteomic and genomic data. The random proteomic GD pattern, which is characterized by decreased cell adhesion and lower levels of tumor-interacting endothelial cells, was significantly associated with increased risk of recurrence or death in two independent patient cohorts. Our study presents comprehensive spatial mapping of ITH in lung adenocarcinoma and provides insights into the mechanisms and clinical consequences of GD.

Stepwise-edited, human melanoma models reveal mutations' effect on tumor and microenvironment.

Establishing causal relationships between genetic alterations of human cancers and specific phenotypes of malignancy remains a challenge. We sequentially introduced mutations into healthy human melanocytes in up to five genes spanning six commonly disrupted melanoma pathways, forming nine genetically distinct cellular models of melanoma. We connected mutant melanocyte genotypes to malignant cell expression programs in vitro and in vivo, replicative immortality, malignancy, rapid tumor growth, pigmentation, metastasis, and histopathology. Mutations in malignant cells also affected tumor microenvironment composition and cell states. Our melanoma models shared genotype-associated expression programs with patient melanomas, and a deep learning model showed that these models partially recapitulated genotype-associated histopathological features as well. Thus, a progressive series of genome-edited human cancer models can causally connect genotypes carrying multiple mutations to phenotype.

The Spatial Landscape of Progression and Immunoediting in Primary Melanoma at Single-Cell Resolution.

Cutaneous melanoma is a highly immunogenic malignancy that is surgically curable at early stages but life-threatening when metastatic. Here we integrate high-plex imaging, 3D high-resolution microscopy, and spatially resolved microregion transcriptomics to study immune evasion and immunoediting in primary melanoma. We find that recurrent cellular neighborhoods involving tumor, immune, and stromal cells change significantly along a progression axis involving precursor states, melanoma in situ, and invasive tumor. Hallmarks of immunosuppression are already detectable in precursor regions. When tumors become locally invasive, a consolidated and spatially restricted suppressive environment forms along the tumor-stromal boundary. This environment is established by cytokine gradients that promote expression of MHC-II and IDO1, and by PD1-PDL1-mediated cell contacts involving macrophages, dendritic cells, and T cells. A few millimeters away, cytotoxic T cells synapse with melanoma cells in fields of tumor regression. Thus, invasion and immunoediting can coexist within a few millimeters of each other in a single specimen. SIGNIFICANCE: The reorganization of the tumor ecosystem in primary melanoma is an excellent setting in which to study immunoediting and immune evasion. Guided by classic histopathology, spatial profiling of proteins and mRNA reveals recurrent morphologic and molecular features of tumor evolution that involve localized paracrine cytokine signaling and direct cell-cell contact. This article is highlighted in the In This Issue feature, p. 1397.

MCMICRO: a scalable, modular image-processing pipeline for multiplexed tissue imaging.

Highly multiplexed tissue imaging makes detailed molecular analysis of single cells possible in a preserved spatial context. However, reproducible analysis of large multichannel images poses a substantial computational challenge. Here, we describe a modular and open-source computational pipeline, MCMICRO, for performing the sequential steps needed to transform whole-slide images into single-cell data. We demonstrate the use of MCMICRO on tissue and tumor images acquired using multiple imaging platforms, thereby providing a solid foundation for the continued development of tissue imaging software.

Obesity Shapes Metabolism in the Tumor Microenvironment to Suppress Anti-Tumor Immunity.

Obesity is a major cancer risk factor, but how differences in systemic metabolism change the tumor microenvironment (TME) and impact anti-tumor immunity is not understood. Here, we demonstrate that high-fat diet (HFD)-induced obesity impairs CD8+ T cell function in the murine TME, accelerating tumor growth. We generate a single-cell resolution atlas of cellular metabolism in the TME, detailing how it changes with diet-induced obesity. We find that tumor and CD8+ T cells display distinct metabolic adaptations to obesity. Tumor cells increase fat uptake with HFD, whereas tumor-infiltrating CD8+ T cells do not. These differential adaptations lead to altered fatty acid partitioning in HFD tumors, impairing CD8+ T cell infiltration and function. Blocking metabolic reprogramming by tumor cells in obese mice improves anti-tumor immunity. Analysis of human cancers reveals similar transcriptional changes in CD8+ T cell markers, suggesting interventions that exploit metabolism to improve cancer immunotherapy.

Vascular Disease and Thrombosis in SARS-CoV-2-Infected Rhesus Macaques.

The COVID-19 pandemic has led to extensive morbidity and mortality throughout the world. Clinical features that drive SARS-CoV-2 pathogenesis in humans include inflammation and thrombosis, but the mechanistic details underlying these processes remain to be determined. In this study, we demonstrate endothelial disruption and vascular thrombosis in histopathologic sections of lungs from both humans and rhesus macaques infected with SARS-CoV-2. To define key molecular pathways associated with SARS-CoV-2 pathogenesis in macaques, we performed transcriptomic analyses of bronchoalveolar lavage and peripheral blood and proteomic analyses of serum. We observed macrophage infiltrates in lung and upregulation of macrophage, complement, platelet activation, thrombosis, and proinflammatory markers, including C-reactive protein, MX1, IL-6, IL-1, IL-8, TNFα, and NF-κB. These results suggest a model in which critical interactions between inflammatory and thrombosis pathways lead to SARS-CoV-2-induced vascular disease. Our findings suggest potential therapeutic targets for COVID-19.

HDAC6 mediates an aggresome-like mechanism for NLRP3 and pyrin inflammasome activation.

Inflammasomes are supramolecular complexes that play key roles in immune surveillance. This is accomplished by the activation of inflammatory caspases, which leads to the proteolytic maturation of interleukin 1ß (IL-1ß) and pyroptosis. Here, we show that nucleotide-binding domain, leucine-rich repeat, and pyrin domain-containing protein 3 (NLRP3)- and pyrin-mediated inflammasome assembly, caspase activation, and IL-1ß conversion occur at the microtubule-organizing center (MTOC). Furthermore, the dynein adapter histone deacetylase 6 (HDAC6) is indispensable for the microtubule transport and assembly of these inflammasomes both in vitro and in mice. Because HDAC6 can transport ubiquitinated pathological aggregates to the MTOC for aggresome formation and autophagosomal degradation, its role in NLRP3 and pyrin inflammasome activation also provides an inherent mechanism for the down-regulation of these inflammasomes by autophagy. This work suggests an unexpected parallel between the formation of physiological and pathological aggregates.

Author Correction: Immunogenomic profiling determines responses to combined PARP and PD-1 inhibition in ovarian cancer.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

Immunogenomic profiling determines responses to combined PARP and PD-1 inhibition in ovarian cancer.

Combined PARP and immune checkpoint inhibition has yielded encouraging results in ovarian cancer, but predictive biomarkers are lacking. We performed immunogenomic profiling and highly multiplexed single-cell imaging on tumor samples from patients enrolled in a Phase I/II trial of niraparib and pembrolizumab in ovarian cancer (NCT02657889). We identify two determinants of response; mutational signature 3 reflecting defective homologous recombination DNA repair, and positive immune score as a surrogate of interferon-primed exhausted CD8 + T-cells in the tumor microenvironment. Presence of one or both features associates with an improved outcome while concurrent absence yields no responses. Single-cell spatial analysis reveals prominent interactions of exhausted CD8 + T-cells and PD-L1 + macrophages and PD-L1 + tumor cells as mechanistic determinants of response. Furthermore, spatial analysis of two extreme responders shows differential clustering of exhausted CD8 + T-cells with PD-L1 + macrophages in the first, and exhausted CD8 + T-cells with cancer cells harboring genomic PD-L1 and PD-L2 amplification in the second.

Highly multiplexed immunofluorescence images and single-cell data of immune markers in tonsil and lung cancer.

In this data descriptor, we document a dataset of multiplexed immunofluorescence images and derived single-cell measurements of immune lineage and other markers in formaldehyde-fixed and paraffin-embedded (FFPE) human tonsil and lung cancer tissue. We used tissue cyclic immunofluorescence (t-CyCIF) to generate fluorescence images which we artifact corrected using the BaSiC tool, stitched and registered using the ASHLAR algorithm, and segmented using ilastik software and MATLAB. We extracted single-cell features from these images using HistoCAT software. The resulting dataset can be visualized using image browsers and analyzed using high-dimensional, single-cell methods. This dataset is a valuable resource for biological discovery of the immune system in normal and diseased states as well as for the development of multiplexed image analysis and viewing tools.

Qualifying antibodies for image-based immune profiling and multiplexed tissue imaging.

Multiplexed tissue imaging enables precise, spatially resolved enumeration and characterization of cell types and states in human resection specimens. A growing number of methods applicable to formalin-fixed, paraffin-embedded (FFPE) tissue sections have been described, the majority of which rely on antibodies for antigen detection and mapping. This protocol provides step-by-step procedures for confirming the selectivity and specificity of antibodies used in fluorescence-based tissue imaging and for the construction and validation of antibody panels. Although the protocol is implemented using tissue-based cyclic immunofluorescence (t-CyCIF) as an imaging platform, these antibody-testing methods are broadly applicable. We demonstrate assembly of a 16-antibody panel for enumerating and localizing T cells and B cells, macrophages, and cells expressing immune checkpoint regulators. The protocol is accessible to individuals with experience in microscopy and immunofluorescence; some experience in computation is required for data analysis. A typical 30-antibody dataset for 20 FFPE slides can be generated within 2 weeks.

Extracellular poly(ADP-ribose) is a pro-inflammatory signal for macrophages.

Poly(ADP-ribose) polymerase 1 (PARP1) synthesizes poly(ADP-ribose) (PAR), an essential post-translational modification whose function is important in many cellular processes including DNA damage signaling, cell death, and inflammation. All known PAR biology is intracellular, but we suspected it might also play a role in cell-to-cell communication during inflammation. We found that PAR activated cytokine release in human and mouse macrophages, a hallmark of innate immune activation, and determined structure-activity relationships. PAR was rapidly internalized by murine macrophages, while the monomer, ADP-ribose, was not. Inhibitors of Toll-like receptor 2 (TLR2) and TLR4 signaling blocked macrophage responses to PAR, and PAR induced TLR2 and TLR4 signaling in reporter cell lines suggesting it was recognized by these TLRs, much like bacterial pathogens. We propose that PAR acts as an extracellular damage associated molecular pattern that drives inflammatory signaling.

Hydrolysis of 2'3'-cGAMP by ENPP1 and design of nonhydrolyzable analogs.

Agonists of mouse STING (TMEM173) shrink and even cure solid tumors by activating innate immunity; human STING (hSTING) agonists are needed to test this therapeutic hypothesis in humans. The endogenous STING agonist is 2'3'-cGAMP, a second messenger that signals the presence of cytosolic double-stranded DNA. We report activity-guided partial purification and identification of ecto-nucleotide pyrophosphatase/phosphodiesterase (ENPP1) to be the dominant 2'3'-cGAMP hydrolyzing activity in cultured cells. The hydrolysis activity of ENPP1 was confirmed using recombinant protein and was depleted in tissue extracts and plasma from Enpp1(-/-) mice. We synthesized a hydrolysis-resistant bisphosphothioate analog of 2'3'-cGAMP (2'3'-cG(s)A(s)MP) that has similar affinity for hSTING in vitro and is ten times more potent at inducing IFN-ß secretion from human THP1 monocytes. Studies in mouse Enpp1(-/-) lung fibroblasts indicate that resistance to hydrolysis contributes substantially to its higher potency. 2'3'-cG(s)A(s)MP is therefore improved over natural 2'3'-cGAMP as a model agonist and has potential as a vaccine adjuvant and cancer therapeutic.

Anticancer flavonoids are mouse-selective STING agonists.

The flavonoids FAA and DMXAA showed impressive activity against solid tumors in mice but failed clinical trials. They act on a previously unknown molecular target(s) to trigger cytokine release from leukocytes, which causes tumor-specific vascular damage and other antitumor effects. We show that DMXAA is a competitive agonist ligand for mouse STING (stimulator of interferon genes), a receptor for the bacterial PAMP cyclic-di-GMP (c-di-GMP) and an endogenous second messenger cyclic-GMP-AMP. In our structure-activity relationship studies, STING binding affinity and pathway activation activity of four flavonoids correlated with activity in a mouse tumor model measured previously. We propose that STING agonist activity accounts for the antitumor effects of FAA and DMXAA in mice. Importantly, DMXAA does not bind to human STING, which may account for its lack of efficacy or mechanism-related toxicity in man. We propose that STING is a druggable target for a novel innate immune activation mechanism of chemotherapy.

A genomic toolkit to investigate kinesin and myosin motor function in cells.

Coordination of multiple kinesin and myosin motors is required for intracellular transport, cell motility and mitosis. However, comprehensive resources that allow systems analysis of the localization and interplay between motors in living cells do not exist. Here, we generated a library of 243 amino- and carboxy-terminally tagged mouse and human bacterial artificial chromosome transgenes to establish 227 stably transfected HeLa cell lines, 15 mouse embryonic stem cell lines and 1 transgenic mouse line. The cells were characterized by expression and localization analyses and further investigated by affinity-purification mass spectrometry, identifying 191 candidate protein-protein interactions. We illustrate the power of this resource in two ways. First, by characterizing a network of interactions that targets CEP170 to centrosomes, and second, by showing that kinesin light-chain heterodimers bind conventional kinesin in cells. Our work provides a set of validated resources and candidate molecular pathways to investigate motor protein function across cell lineages.

Novel microcephalic primordial dwarfism disorder associated with variants in the centrosomal protein ninein.

CONTEXT: Microcephalic primordial dwarfism (MPD) is a rare, severe form of human growth failure in which growth restriction is evident in utero and continues into postnatal life. Single causative gene defects have been identified in a number of patients with MPD, and all involve genes fundamental to cellular processes including centrosome functions. OBJECTIVE: The objective of the study was to find the genetic etiology of a novel presentation of MPD. DESIGN: The design of the study was whole-exome sequencing performed on two affected sisters in a single family. Molecular and functional studies of a candidate gene were performed using patient-derived primary fibroblasts and a zebrafish morpholino oligonucleotides knockdown model. PATIENTS: Two sisters presented with a novel subtype of MPD, including severe intellectual disabilities. MAIN OUTCOME MEASURES: NIN, encoding Ninein, a centrosomal protein critically involved in asymmetric cell division, was identified as a candidate gene, and functional impacts in fibroblasts and zebrafish were studied. RESULTS: From 34,606 genomic variants, two very rare missense variants in NIN were identified. Both probands were compound heterozygotes. In the zebrafish, ninein knockdown led to specific and novel defects in the specification and morphogenesis of the anterior neuroectoderm, resulting in a deformity of the developing cranium with a small, squared skull highly reminiscent of the human phenotype. CONCLUSION: We identified a novel clinical subtype of MPD in two sisters who have rare variants in NIN. We show, for the first time, that reduction of ninein function in the developing zebrafish leads to specific deficiencies of brain and skull development, offering a developmental basis for the myriad phenotypes in our patients.