A Single-Cell Atlas of the Tumor and Immune Ecosystem of Human Breast Cancer.

Breast cancer is a heterogeneous disease. Tumor cells and associated healthy cells form ecosystems that determine disease progression and response to therapy. To characterize features of breast cancer ecosystems and their associations with clinical data, we analyzed 144 human breast tumor and 50 non-tumor tissue samples using mass cytometry. The expression of 73 proteins in 26 million cells was evaluated using tumor and immune cell-centric antibody panels. Tumors displayed individuality in tumor cell composition, including phenotypic abnormalities and phenotype dominance. Relationship analyses between tumor and immune cells revealed characteristics of ecosystems related to immunosuppression and poor prognosis. High frequencies of PD-L1+ tumor-associated macrophages and exhausted T cells were found in high-grade ER+ and ER- tumors. This large-scale, single-cell atlas deepens our understanding of breast tumor ecosystems and suggests that ecosystem-based patient classification will facilitate identification of individuals for precision medicine approaches targeting the tumor and its immunoenvironment.

An Immune Atlas of Clear Cell Renal Cell Carcinoma.

Immune cells in the tumor microenvironment modulate cancer progression and are attractive therapeutic targets. Macrophages and T cells are key components of the microenvironment, yet their phenotypes and relationships in this ecosystem and to clinical outcomes are ill defined. We used mass cytometry with extensive antibody panels to perform in-depth immune profiling of samples from 73 clear cell renal cell carcinoma (ccRCC) patients and five healthy controls. In 3.5 million measured cells, we identified 17 tumor-associated macrophage phenotypes, 22 T cell phenotypes, and a distinct immune composition correlated with progression-free survival, thereby presenting an in-depth human atlas of the immune tumor microenvironment in this disease. This study revealed potential biomarkers and targets for immunotherapy development and validated tools that can be used for immune profiling of other tumor types.

Kinome-wide decoding of network-attacking mutations rewiring cancer signaling.

Cancer cells acquire pathological phenotypes through accumulation of mutations that perturb signaling networks. However, global analysis of these events is currently limited. Here, we identify six types of network-attacking mutations (NAMs), including changes in kinase and SH2 modulation, network rewiring, and the genesis and extinction of phosphorylation sites. We developed a computational platform (ReKINect) to identify NAMs and systematically interpreted the exomes and quantitative (phospho-)proteomes of five ovarian cancer cell lines and the global cancer genome repository. We identified and experimentally validated several NAMs, including PKCγ M501I and PKD1 D665N, which encode specificity switches analogous to the appearance of kinases de novo within the kinome. We discover mutant molecular logic gates, a drift toward phospho-threonine signaling, weakening of phosphorylation motifs, and kinase-inactivating hotspots in cancer. Our method pinpoints functional NAMs, scales with the complexity of cancer genomes and cell signaling, and may enhance our capability to therapeutically target tumor-specific networks.

High-Dimensional T Helper Cell Profiling Reveals a Broad Diversity of Stably Committed Effector States and Uncovers Interlineage Relationships.

CD4+ T helper (Th) cells are fundamental players in immunity. Based on the expression of signature cytokines and transcription factors, several Th subsets have been defined. Th cells are thought to be far more heterogeneous and multifunctional than originally believed, but characterization of the full diversity has been hindered by technical limitations. Here, we employ mass cytometry to analyze the diversity of Th cell responses generated in vitro and in animal disease models, revealing a vast heterogeneity of effector states with distinct cytokine footprints. The diversities of cytokine responses established during primary antigen encounters in Th1- and Th2-cell-polarizing conditions are largely maintained after secondary challenge, regardless of the new inflammatory environment, highlighting many of the identified states as stable Th cell sublineages. We also find that Th17 cells tend to upregulate Th2-cell-associated cytokines upon challenge, indicating a closer developmental connection between Th17 and Th2 cells than previously anticipated.

Dual specificity kinase DYRK3 couples stress granule condensation/dissolution to mTORC1 signaling.

Cytosolic compartmentalization through liquid-liquid unmixing, such as the formation of RNA granules, is involved in many cellular processes and might be used to regulate signal transduction. However, specific molecular mechanisms by which liquid-liquid unmixing and signal transduction are coupled remain unknown. Here, we show that during cellular stress the dual specificity kinase DYRK3 regulates the stability of P-granule-like structures and mTORC1 signaling. DYRK3 displays a cyclic partitioning mechanism between stress granules and the cytosol via a low-complexity domain in its N terminus and its kinase activity. When DYRK3 is inactive, it prevents stress granule dissolution and the release of sequestered mTORC1. When DYRK3 is active, it allows stress granule dissolution, releasing mTORC1 for signaling and promoting its activity by directly phosphorylating the mTORC1 inhibitor PRAS40. This mechanism links cytoplasmic compartmentalization via liquid phase transitions with cellular signaling.

Analysis of the Human Kinome and Phosphatome by Mass Cytometry Reveals Overexpression-Induced Effects on Cancer-Related Signaling.

Kinase and phosphatase overexpression drives tumorigenesis and drug resistance. We previously developed a mass-cytometry-based single-cell proteomics approach that enables quantitative assessment of overexpression effects on cell signaling. Here, we applied this approach in a human kinome- and phosphatome-wide study to assess how 649 individually overexpressed proteins modulated cancer-related signaling in HEK293T cells in an abundance-dependent manner. Based on these data, we expanded the functional classification of human kinases and phosphatases and showed that the overexpression effects include non-catalytic roles. We detected 208 previously unreported signaling relationships. The signaling dynamics analysis indicated that the overexpression of ERK-specific phosphatases sustains proliferative signaling. This suggests a phosphatase-driven mechanism of cancer progression. Moreover, our analysis revealed a drug-resistant mechanism through which overexpression of tyrosine kinases, including SRC, FES, YES1, and BLK, induced MEK-independent ERK activation in melanoma A375 cells. These proteins could predict drug sensitivity to BRAF-MEK concurrent inhibition in cells carrying BRAF mutations.

DNA-barcoded signal amplification for imaging mass cytometry enables sensitive and highly multiplexed tissue imaging.

Imaging mass cytometry (IMC) is a highly multiplexed, antibody-based imaging method that captures heterogeneous spatial protein expression patterns at subcellular resolution. Here we report the extension of IMC to low-abundance markers through incorporation of the DNA-based signal amplification by exchange reaction, immuno-SABER. We applied SABER-IMC to image the tumor immune microenvironment in human melanoma by simultaneous imaging of 18 markers with immuno-SABER and 20 markers without amplification. SABER-IMC enabled the identification of immune cell phenotypic markers, such as T cell co-receptors and their ligands, that are not detectable with IMC.

Optimizing multiplexed imaging experimental design through tissue spatial segregation estimation.

Recent advances in multiplexed imaging methods allow simultaneous detection of dozens of proteins and hundreds of RNAs, enabling deep spatial characterization of both healthy and diseased tissues. Parameters for the design of optimal multiplex imaging studies, especially those estimating how much area has to be imaged to capture all cell phenotype clusters, are lacking. Here, using a spatial transcriptomic atlas of healthy and tumor human tissues, we developed a statistical framework that determines the number and area of fields of view necessary to accurately identify all cell phenotypes that are part of a tissue. Using this strategy on imaging mass cytometry data, we identified a measurement of tissue spatial segregation that enables optimal experimental design. This strategy will enable an improved design of multiplexed imaging studies.

Multielement Z-tag imaging by X-ray fluorescence microscopy for next-generation multiplex imaging.

Rapid, highly multiplexed, nondestructive imaging that spans the molecular to the supra-cellular scale would be a powerful tool for tissue analysis. However, the physical constraints of established imaging methods limit the simultaneous improvement of these parameters. Whole-organism to atomic-level imaging is possible with tissue-penetrant, picometer-wavelength X-rays. To enable highly multiplexed X-ray imaging, we developed multielement Z-tag X-ray fluorescence (MEZ-XRF) that can operate at kHz speeds when combined with signal amplification by exchange reaction (SABER)-amplified Z-tag reagents. We demonstrated parallel imaging of 20 Z-tag or SABER Z-tag reagents at subcellular resolution in cell lines and multiple human tissues. We benchmarked MEZ-XRF against imaging mass cytometry and demonstrated the nondestructive multiscale repeat imaging capabilities of MEZ-XRF with rapid tissue overview scans, followed by slower, more sensitive imaging of low-abundance markers such as immune checkpoint proteins. The unique multiscale, nondestructive nature of MEZ-XRF, combined with SABER Z-tags for high sensitivity or enhanced speed, enables highly multiplexed bioimaging across biological scales.

Phosphorylation of Nup98 by multiple kinases is crucial for NPC disassembly during mitotic entry.

Disassembly of nuclear pore complexes (NPCs) is a decisive event during mitotic entry in cells undergoing open mitosis, yet the molecular mechanisms underlying NPC disassembly are unknown. Using chemical inhibition and depletion experiments we show that NPC disassembly is a phosphorylation-driven process, dependent on CDK1 activity and supported by members of the NIMA-related kinase (Nek) family. We identify phosphorylation of the GLFG-repeat nucleoporin Nup98 as an important step in mitotic NPC disassembly. Mitotic hyperphosphorylation of Nup98 is accomplished by multiple kinases, including CDK1 and Neks. Nuclei carrying a phosphodeficient mutant of Nup98 undergo nuclear envelope breakdown slowly, such that both the dissociation of Nup98 from NPCs and the permeabilization of the nuclear envelope are delayed. Together, our data provide evidence for a phosphorylation-dependent mechanism underlying disintegration of NPCs during prophase. Moreover, we identify mitotic phosphorylation of Nup98 as a rate-limiting step in mitotic NPC disassembly.

The single-cell pathology landscape of breast cancer.

Single-cell analyses have revealed extensive heterogeneity between and within human tumours1-4, but complex single-cell phenotypes and their spatial context are not at present reflected in the histological stratification that is the foundation of many clinical decisions. Here we use imaging mass cytometry5 to simultaneously quantify 35 biomarkers, resulting in 720 high-dimensional pathology images of tumour tissue from 352 patients with breast cancer, with long-term survival data available for 281 patients. Spatially resolved, single-cell analysis identified the phenotypes of tumour and stromal single cells, their organization and their heterogeneity, and enabled the cellular architecture of breast cancer tissue to be characterized on the basis of cellular composition and tissue organization. Our analysis reveals multicellular features of the tumour microenvironment and novel subgroups of breast cancer that are associated with distinct clinical outcomes. Thus, spatially resolved, single-cell analysis can characterize intratumour phenotypic heterogeneity in a disease-relevant manner, with the potential to inform patient-specific diagnosis.

cytoviewer: an R/Bioconductor package for interactive visualization and exploration of highly multiplexed imaging data.

BACKGROUND: Highly multiplexed imaging enables single-cell-resolved detection of numerous biological molecules in their spatial tissue context. Interactive visualization of multiplexed imaging data is crucial at any step of data analysis to facilitate quality control and the spatial exploration of single cell features. However, tools for interactive visualization of multiplexed imaging data are not available in the statistical programming language R. RESULTS: Here, we describe cytoviewer, an R/Bioconductor package for interactive visualization and exploration of multi-channel images and segmentation masks. The cytoviewer package supports flexible generation of image composites, allows side-by-side visualization of single channels, and facilitates the spatial visualization of single-cell data in the form of segmentation masks. As such, cytoviewer improves image and segmentation quality control, the visualization of cell phenotyping results and qualitative validation of hypothesis at any step of data analysis. The package operates on standard data classes of the Bioconductor project and therefore integrates with an extensive framework for single-cell and image analysis. The graphical user interface allows intuitive navigation and little coding experience is required to use the package. We showcase the functionality and biological application of cytoviewer by analysis of an imaging mass cytometry dataset acquired from cancer samples. CONCLUSIONS: The cytoviewer package offers a rich set of features for highly multiplexed imaging data visualization in R that seamlessly integrates with the workflow for image and single-cell data analysis. It can be installed from Bioconductor via https://www.bioconductor.org/packages/release/bioc/html/cytoviewer.html . The development version and further instructions can be found on GitHub at https://github.com/BodenmillerGroup/cytoviewer .

Full dynamic range proteome analysis of S. cerevisiae by targeted proteomics.

The rise of systems biology implied a growing demand for highly sensitive techniques for the fast and consistent detection and quantification of target sets of proteins across multiple samples. This is only partly achieved by classical mass spectrometry or affinity-based methods. We applied a targeted proteomics approach based on selected reaction monitoring (SRM) to detect and quantify proteins expressed to a concentration below 50 copies/cell in total S. cerevisiae digests. The detection range can be extended to single-digit copies/cell and to proteins undetected by classical methods. We illustrate the power of the technique by the consistent and fast measurement of a network of proteins spanning the entire abundance range over a growth time course of S. cerevisiae transiting through a series of metabolic phases. We therefore demonstrate the potential of SRM-based proteomics to provide assays for the measurement of any set of proteins of interest in yeast at high-throughput and quantitative accuracy.

Regulation of PKD by the MAPK p38delta in insulin secretion and glucose homeostasis.

Dysfunction and loss of insulin-producing pancreatic beta cells represent hallmarks of diabetes mellitus. Here, we show that mice lacking the mitogen-activated protein kinase (MAPK) p38delta display improved glucose tolerance due to enhanced insulin secretion from pancreatic beta cells. Deletion of p38delta results in pronounced activation of protein kinase D (PKD), the latter of which we have identified as a pivotal regulator of stimulated insulin exocytosis. p38delta catalyzes an inhibitory phosphorylation of PKD1, thereby attenuating stimulated insulin secretion. In addition, p38delta null mice are protected against high-fat-feeding-induced insulin resistance and oxidative stress-mediated beta cell failure. Inhibition of PKD1 reverses enhanced insulin secretion from p38delta-deficient islets and glucose tolerance in p38delta null mice as well as their susceptibility to oxidative stress. In conclusion, the p38delta-PKD pathway integrates regulation of the insulin secretory capacity and survival of pancreatic beta cells, pointing to a pivotal role for this pathway in the development of overt diabetes mellitus.

Uncovering axes of variation among single-cell cancer specimens.

While several tools have been developed to map axes of variation among individual cells, no analogous approaches exist for identifying axes of variation among multicellular biospecimens profiled at single-cell resolution. For this purpose, we developed 'phenotypic earth mover's distance' (PhEMD). PhEMD is a general method for embedding a 'manifold of manifolds', in which each datapoint in the higher-level manifold (of biospecimens) represents a collection of points that span a lower-level manifold (of cells). We apply PhEMD to a newly generated drug-screen dataset and demonstrate that PhEMD uncovers axes of cell subpopulational variation among a large set of perturbation conditions. Moreover, we show that PhEMD can be used to infer the phenotypes of biospecimens not directly profiled. Applied to clinical datasets, PhEMD generates a map of the patient-state space that highlights sources of patient-to-patient variation. PhEMD is scalable, compatible with leading batch-effect correction techniques and generalizable to multiple experimental designs.

cytomapper: an R/Bioconductor package for visualization of highly multiplexed imaging data.

SUMMARY: Highly multiplexed imaging technologies enable spatial profiling of dozens of biomarkers in situ. Here, we describe cytomapper, a computational tool written in R, that enables visualization of pixel- and cell-level information obtained by multiplexed imaging. To illustrate its utility, we analysed 100 images obtained by imaging mass cytometry from a cohort of type 1 diabetes patients. In addition, cytomapper includes a Shiny application that allows hierarchical gating of cells based on marker expression and visualization of selected cells in corresponding images. AVAILABILITY AND IMPLEMENTATION: The cytomapper package can be installed via https://www.bioconductor.org/packages/release/bioc/html/cytomapper.html. Code for analysis and further instructions can be found at https://github.com/BodenmillerGroup/cytomapper_publication. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Cell-to-cell and type-to-type heterogeneity of signaling networks: insights from the crowd.

Recent technological developments allow us to measure the status of dozens of proteins in individual cells. This opens the way to understand the heterogeneity of complex multi-signaling networks across cells and cell types, with important implications to understand and treat diseases such as cancer. These technologies are, however, limited to proteins for which antibodies are available and are fairly costly, making predictions of new markers and of existing markers under new conditions a valuable alternative. To assess our capacity to make such predictions and boost further methodological development, we organized the Single Cell Signaling in Breast Cancer DREAM challenge. We used a mass cytometry dataset, covering 36 markers in over 4,000 conditions totaling 80 million single cells across 67 breast cancer cell lines. Through four increasingly difficult subchallenges, the participants predicted missing markers, new conditions, and the time-course response of single cells to stimuli in the presence and absence of kinase inhibitors. The challenge results show that despite the stochastic nature of signal transduction in single cells, the signaling events are tightly controlled and machine learning methods can accurately predict new experimental data.

T-cell recovery and evidence of persistent immune activation 12 months after severe COVID-19.

BACKGROUND: T-cell lymphopenia and functional impairment is a hallmark of severe acute coronavirus disease 2019 (COVID-19). How T-cell numbers and function evolve at later timepoints after clinical recovery remains poorly investigated. METHODS: We prospectively enrolled and longitudinally sampled 173 individuals with asymptomatic to critical COVID-19 and analyzed phenotypic and functional characteristics of T cells using flow cytometry, 40-parameter mass cytometry, targeted proteomics, and functional assays. RESULTS: The extensive T-cell lymphopenia observed particularly in patients with severe COVID-19 during acute infection had recovered 6 months after infection, which was accompanied by a normalization of functional T-cell responses to common viral antigens. We detected persisting CD4+ and CD8+ T-cell activation up to 12 months after infection, in patients with mild and severe COVID-19, as measured by increased HLA-DR and CD38 expression on these cells. Persistent T-cell activation after COVID-19 was independent of administration of a COVID-19 vaccine post-infection. Furthermore, we identified a subgroup of patients with severe COVID-19 that presented with persistently low CD8+ T-cell counts at follow-up and exhibited a distinct phenotype during acute infection consisting of a dysfunctional T-cell response and signs of excessive pro-inflammatory cytokine production. CONCLUSION: Our study suggests that T-cell numbers and function recover in most patients after COVID-19. However, we find evidence of persistent T-cell activation up to 12 months after infection and describe a subgroup of severe COVID-19 patients with persistently low CD8+ T-cell counts exhibiting a dysregulated immune response during acute infection.

Cutaneous and systemic hyperinflammation drives maculopapular drug exanthema in severely ill COVID-19 patients.

BACKGROUND: Coronavirus disease-2019 (COVID-19) has been associated with cutaneous findings, some being the result of drug hypersensitivity reactions such as maculopapular drug rashes (MDR). The aim of this study was to investigate whether COVID-19 may impact the development of the MDR. METHODS: Blood and skin samples from COVID-19 patients (based on a positive nasopharyngeal PCR) suffering from MDR (COVID-MDR), healthy controls, non-COVID-19-related patients with drug rash with eosinophilia and systemic symptoms (DRESS), and MDR were analyzed. We utilized imaging mass cytometry (IMC) to characterize the cellular infiltrate in skin biopsies. Furthermore, RNA sequencing transcriptome of skin biopsy samples and high-throughput multiplexed proteomic profiling of serum were performed. RESULTS: IMC revealed by clustering analyses a more prominent, phenotypically shifted cytotoxic CD8+ T cell population and highly activated monocyte/macrophage (Mo/Mac) clusters in COVID-MDR. The RNA sequencing transcriptome demonstrated a more robust cytotoxic response in COVID-MDR skin. However, severe acute respiratory syndrome coronavirus 2 was not detected in skin biopsies at the time point of MDR diagnosis. Serum proteomic profiling of COVID-MDR patients revealed upregulation of various inflammatory mediators (IL-4, IL-5, IL-6, TNF, and IFN-γ), eosinophil and Mo/Mac -attracting chemokines (MCP-2, MCP-3, MCP-4 and CCL11). Proteomics analyses demonstrated a massive systemic cytokine storm in COVID-MDR compared with the relatively milder cytokine storm observed in DRESS, while MDR did not exhibit such features. CONCLUSION: A systemic cytokine storm may promote activation of Mo/Mac and cytotoxic CD8+ T cells in severe COVID-19 patients, which in turn may impact the development of MDR.

The calcineurin signaling network evolves via conserved kinase-phosphatase modules that transcend substrate identity.

To define a functional network for calcineurin, the conserved Ca(2+)/calmodulin-regulated phosphatase, we systematically identified its substrates in S. cerevisiae using phosphoproteomics and bioinformatics, followed by copurification and dephosphorylation assays. This study establishes new calcineurin functions and reveals mechanisms that shape calcineurin network evolution. Analyses of closely related yeasts show that many proteins were recently recruited to the network by acquiring a calcineurin-recognition motif. Calcineurin substrates in yeast and mammals are distinct due to network rewiring but, surprisingly, are phosphorylated by similar kinases. We postulate that corecognition of conserved substrate features, including phosphorylation and docking motifs, preserves calcineurin-kinase opposition during evolution. One example we document is a composite docking site that confers substrate recognition by both calcineurin and MAPK. We propose that conserved kinase-phosphatase pairs define the architecture of signaling networks and allow other connections between kinases and phosphatases to develop that establish common regulatory motifs in signaling networks.