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Cutaneous T cell lymphomas (CTCL) are rare but aggressive cancers without effective treatments. While a subset of patients derive benefit from PD-1 blockade, there is a critically unmet need for predictive biomarkers of response. Herein, we perform CODEX multiplexed tissue imaging and RNA sequencing on 70 tumor regions from 14 advanced CTCL patients enrolled in a pembrolizumab clinical trial (NCT02243579). We find no differences in the frequencies of immune or tumor cells between responders and non-responders. Instead, we identify topographical differences between effector PD-1 + CD4 + T cells, tumor cells, and immunosuppressive Tregs, from which we derive a spatial biomarker, termed the SpatialScore , that correlates strongly with pembrolizumab response in CTCL. The SpatialScore coincides with differences in the functional immune state of the tumor microenvironment, T cell function, and tumor cell-specific chemokine recruitment and is validated using a simplified, clinically accessible tissue imaging platform. Collectively, these results provide a paradigm for investigating the spatial balance of effector and suppressive T cell activity and broadly leveraging this biomarker approach to inform the clinical use of immunotherapies. PD-1 blockade is effective for only a subset of patients with cutaneous T cell lymphomas. Here, the authors report a spatial biomarker that uses immune and cancer cell topography to predict response to PD-1 blockade in this disease.

Background Algorithmic cellular segmentation is an essential step for the quantitative analysis of highly multiplexed tissue images. Current segmentation pipelines often require manual dataset annotation and additional training, significant parameter tuning, or a sophisticated understanding of programming to adapt the software to the researcher’s need. Here, we present CellSeg, an open-source, pre-trained nucleus segmentation and signal quantification software based on the Mask region-convolutional neural network (R-CNN) architecture. CellSeg is accessible to users with a wide range of programming skills. Results CellSeg performs at the level of top segmentation algorithms in the 2018 Kaggle Data Challenge both qualitatively and quantitatively and generalizes well to a diverse set of multiplexed imaged cancer tissues compared to established state-of-the-art segmentation algorithms. Automated segmentation post-processing steps in the CellSeg pipeline improve the resolution of immune cell populations for downstream single-cell analysis. Finally, an application of CellSeg to a highly multiplexed colorectal cancer dataset acquired on the CO-Detection by indEXing (CODEX) platform demonstrates that CellSeg can be integrated into a multiplexed tissue imaging pipeline and lead to accurate identification of validated cell populations. Conclusion CellSeg is a robust cell segmentation software for analyzing highly multiplexed tissue images, accessible to biology researchers of any programming skill level.

In autoimmune type 1 diabetes (T1D), immune cells infiltrate and destroy the islets of Langerhans — islands of endocrine tissue dispersed throughout the pancreas. However, the contribution of cellular programs outside islets to insulitis is unclear. Here, using CO-Detection by indEXing (CODEX) tissue imaging and cadaveric pancreas samples, we simultaneously examine islet and extra-islet inflammation in human T1D. We identify four sub-states of inflamed islets characterized by the activation profiles of CD8 + T cells enriched in islets relative to the surrounding tissue. We further find that the extra-islet space of lobules with extensive islet-infiltration differs from the extra-islet space of less infiltrated areas within the same tissue section. Finally, we identify lymphoid structures away from islets enriched in CD45RA + T cells — a population also enriched in one of the inflamed islet sub-states. Together, these data help define the coordination between islets and the extra-islet pancreas in the pathogenesis of human T1D. When someone has type 1 diabetes, their immune system mistakenly targets and destroys β-cells in the pancreas, which produce insulin, the hormone that helps bring down sugar levels in the blood after we eat. Despite advances in treatment, most people with type 1 diabetes will depend on insulin for their entire lives. T cells are a type of immune cell involved in type 1 diabetes. These cells infiltrate the pancreatic islets, the structures where β-cells reside, to attack the β-cells. This process, called insulitis, is poorly understood, partly because obtaining tissue samples containing islets in the process of being infiltrated by T cells is extremely difficult. Barlow et al. collaborated with the Network for Pancreatic Organ Donors with Diabetes to obtain pancreatic tissues from eight organ donors with type 1 diabetes, and two organ donors whose immune systems could recognize islets but who were not yet exhibiting diabetes symptoms. Barlow et al. analysed 54 proteins in each tissue section and examined how inflammation progressed in islet cells and the surrounding pancreas to better understand insulitis. The researchers identified four types of insulitis, each defined by the types of T cells present. The nature of the T cells in islets is important because it may affect how fast type 1 diabetes progresses. Although Barlow et al. did not examine enough cases to establish if a correlation exists between the types of insulitis and disease progression, this can be examined in future studies. They also found that, during insulitis, the blood vessels in the islets switched on a protein called IDO, possibly in response to T cells that infiltrate islets. IDO may temporarily protect the islets from the immune response as insulitis progresses, but it is insufficient to protect the β-cells. Barlow et al. further found aggregates of T cells mixed with B cells, another type of immune cell, in the pancreas tissue surrounding the islets. Given that B cells and T cells provide stimulatory signals to each other, these aggregates may promote inflammation and be a new therapeutic target. Barlow et al. also wanted to understand why T cells target some islets more than others and why islet destruction is spatially organized. The team compared pancreatic areas with many inflamed islets to areas in the same donor where fewer islets were inflamed, finding that the cell composition differs. Interestingly, the types of cells that were different were not the same as those that were infiltrating islets. B cells, macrophages and T cells were the major cell types infiltrating islets, but the cells that varied outside islets were nerves, endothelial cells, and a third cell type that may have been innate lymphoid cells. These results indicate a crosstalk between the cells outside islets and those that infiltrate islets. The results by Barlow et al. lay the groundwork for a better understanding of the biology underpinning how the immune system destroys β-cells in insulitis. The next steps would be to see if other cells in the islets can influence T cells and if diabetes could be delayed by inhibiting interactions between T cells and the relevant cells outside the islets. Moreover, it would be important to establish whether preserving IDO in endothelial cells could delay diabetes symptoms.

Disease progression and relapse of chronic myeloid leukemia (CML) are caused by therapy resistant leukemia stem cells (LSCs), and cure relies on their eradication. The microenvironment in the bone marrow (BM) is known to contribute to LSC maintenance and resistance. Although leukemic infiltration of the spleen is a hallmark of CML, it is unknown whether spleen cells form a niche that maintains LSCs. Here, we demonstrate that LSCs preferentially accumulate in the spleen and contribute to disease progression. Spleen LSCs were located in the red pulp close to red pulp macrophages (RPM) in CML patients and in a murine CML model. Pharmacologic and genetic depletion of RPM reduced LSCs and decreased their cell cycling activity in the spleen. Gene expression analysis revealed enriched stemness and decreased myeloid lineage differentiation in spleen leukemic stem and progenitor cells (LSPCs). These results demonstrate that splenic RPM form a niche that maintains CML LSCs in a quiescent state, resulting in disease progression and resistance to therapy.

Influenza is a significant cause of morbidity and mortality worldwide. Here we show changes in the abundance and activation states of more than 50 immune cell subsets in 35 individuals over 11 time points during human A/California/2009 (H1N1) virus challenge monitored using mass cytometry along with other clinical assessments. Peak change in monocyte, B cell, and T cell subset frequencies coincided with peak virus shedding, followed by marked activation of T and NK cells. Results led to the identification of CD38 as a critical regulator of plasmacytoid dendritic cell function in response to influenza virus. Machine learning using study-derived clinical parameters and single-cell data effectively classified and predicted susceptibility to infection. The coordinated immune cell dynamics defined in this study provide a framework for identifying novel correlates of protection in the evaluation of future influenza therapeutics.

The genetic code is a formal principle that determines which proteins an organism can produce from only its genome sequence, without mechanistic modeling. Whether similar formal principles govern the relationship between genome sequence and phenotype across scales - from molecules to cells to tissues - is unknown. Here, we show that a single formal principle - structural correspondence - underlies the relationship between phenotype and genome sequence across scales. We represent phenotypes and the genome as graphs and find mappings between them using structure preservation as the sole constraint. Combinatorial richness in phenotypes more tightly constrains which mappings preserve that structure. Thus, phenotypic structure predicts genetic associations independently of covariation with genotype. This principle rediscovers the amino acid code without prior knowledge of translation or coding sequences, using just one protein and genome sequence as input. We benchmark this principle: applied to phenotypes at the cell, tissue and organ scales, the mappings correctly predict established associations and are driven by transcription factor motifs. Applied to cancer tissue images, we find regulators of spatial gene expression in immune cells. We thus offer a first-principles framework to relate genome sequence with phenotypic structure and guide mechanistic discovery across scales.

In autoimmune type 1 diabetes (T1D), immune cells infiltrate and destroy the islets of Langerhans — islands of endocrine tissue dispersed throughout the pancreas. However, the contribution of cellular programs outside islets to insulitis is unclear. Here, using CO-Detection by indEXing (CODEX) tissue imaging and cadaveric pancreas samples, we simultaneously examine islet and extra-islet inflammation in human T1D. We identify four sub-states of inflamed islets characterized by the activation profiles of CD8 + T cells enriched in islets relative to the surrounding tissue. We further find that the extra-islet space of lobules with extensive islet-infiltration differs from the extra-islet space of less infiltrated areas within the same tissue section. Finally, we identify lymphoid structures away from islets enriched in CD45RA + T cells — a population also enriched in one of the inflamed islet sub-states. Together, these data help define the coordination between islets and the extra-islet pancreas in the pathogenesis of human T1D. When someone has type 1 diabetes, their immune system mistakenly targets and destroys β-cells in the pancreas, which produce insulin, the hormone that helps bring down sugar levels in the blood after we eat. Despite advances in treatment, most people with type 1 diabetes will depend on insulin for their entire lives. T cells are a type of immune cell involved in type 1 diabetes. These cells infiltrate the pancreatic islets, the structures where β-cells reside, to attack the β-cells. This process, called insulitis, is poorly understood, partly because obtaining tissue samples containing islets in the process of being infiltrated by T cells is extremely difficult. Barlow et al. collaborated with the Network for Pancreatic Organ Donors with Diabetes to obtain pancreatic tissues from eight organ donors with type 1 diabetes, and two organ donors whose immune systems could recognize islets but who were not yet exhibiting diabetes symptoms. Barlow et al. analysed 54 proteins in each tissue section and examined how inflammation progressed in islet cells and the surrounding pancreas to better understand insulitis. The researchers identified four types of insulitis, each defined by the types of T cells present. The nature of the T cells in islets is important because it may affect how fast type 1 diabetes progresses. Although Barlow et al. did not examine enough cases to establish if a correlation exists between the types of insulitis and disease progression, this can be examined in future studies. They also found that, during insulitis, the blood vessels in the islets switched on a protein called IDO, possibly in response to T cells that infiltrate islets. IDO may temporarily protect the islets from the immune response as insulitis progresses, but it is insufficient to protect the β-cells. Barlow et al. further found aggregates of T cells mixed with B cells, another type of immune cell, in the pancreas tissue surrounding the islets. Given that B cells and T cells provide stimulatory signals to each other, these aggregates may promote inflammation and be a new therapeutic target. Barlow et al. also wanted to understand why T cells target some islets more than others and why islet destruction is spatially organized. The team compared pancreatic areas with many inflamed islets to areas in the same donor where fewer islets were inflamed, finding that the cell composition differs. Interestingly, the types of cells that were different were not the same as those that were infiltrating islets. B cells, macrophages and T cells were the major cell types infiltrating islets, but the cells that varied outside islets were nerves, endothelial cells, and a third cell type that may have been innate lymphoid cells. These results indicate a crosstalk between the cells outside islets and those that infiltrate islets. The results by Barlow et al. lay the groundwork for a better understanding of the biology underpinning how the immune system destroys β-cells in insulitis. The next steps would be to see if other cells in the islets can influence T cells and if diabetes could be delayed by inhibiting interactions between T cells and the relevant cells outside the islets. Moreover, it would be important to establish whether preserving IDO in endothelial cells could delay diabetes symptoms.

Antitumoral immunity requires organized, spatially nuanced interactions between components of the immune tumor microenvironment (iTME). Understanding this coordinated behavior in effective versus ineffective tumor control will advance immunotherapies. We optimized CO-Detection by indEXing (CODEX) for paraffin-embedded tissue microarrays, enabling profiling of 140 tissue regions from 35 advanced-stage colorectal cancer (CRC) patients with 56 protein markers simultaneously. We identified nine conserved, distinct cellular neighborhoods (CNs) - a collection of components characteristic of the CRC iTME. Enrichment of PD- 1+CD4+ T cells only within a granulocyte CN positively correlated with survival in a high-risk patient subset. Coupling of tumor and immune CNs, fragmentation of T cell and macrophage CNs, and disruption of inter-CN communication was associated with inferior outcomes. This study provides a framework for interrogating complex biological processes, such as antitumoral immunity, demonstrating an example of how tumors can disrupt immune functionality through interference in the concerted action of cells and spatial domains.

Recent therapeutic strategies for glioblastoma (GBM) aim at targeting immune tumor microenvironment (iTME) components to induce antitumoral immunity. A patient-tailored, ex vivo drug testing and response analysis platform for GBM would facilitate personalized therapy planning, provide insights into treatment-induced immune mechanisms in the iTME, and enable the discovery of biomarkers of therapy response and resistance. We cultured 47 GBM explants from tumor center and periphery from 7 patients in perfusion bioreactors to assess iTME responses to immunotherapy. Explants were exposed to antibodies blocking the immune checkpoints CD47, PD-1 or or their combination, and were analyzed by highly multiplexed microscopy (CODEX, co-detection by indexing) using an immune-focused 55-marker panel. Culture media were examined for changes of soluble factors including cytokines, chemokines and metabolites. CODEX enabled the spatially resolved identification and quantification of >850,000 single cells in explants, which were classified into 10 cell types by clustering. Explants from center and periphery differed significantly in their cell type composition, their levels of soluble factors, and their responses to immunotherapy. In a subset of explants, culture media displayed increased interferon-γ levels, which correlated with shifts in immune cell composition within specific tissue compartments, including the enrichment of CD4+ and CD8+ T cells within an adaptive immune compartment. Furthermore, significant differences in the expression levels of functional molecules in innate and adaptive immune cell types were found between explants responding or not to immunotherapy. In non-responder explants, T cells showed higher expression of PD-1, LAG-3, TIM-3 and VISTA, whereas in responders, macrophages and microglia showed higher cathepsin D levels. Our study demonstrates that ex vivo immunotherapy of GBM explants enables an active antitumoral immune response within the tumor center in a subset of patients, and provides a framework for multidimensional personalized assessment of tumor response to immunotherapy.

Simultaneous visualization of the relationship between multiple biomolecules and their ligands or small molecules at the nanometer scale in cells will enable greater understanding of how biological processes operate. We present here high-definition multiplex ion beam imaging (HD-MIBI), a secondary ion mass spectrometry approach capable of high-parameter imaging in 3D of targeted biological entities and exogenously added structurally-unmodified small molecules. With this technology, the atomic constituents of the biomolecules themselves can be used in our system as the “tag” and we demonstrate measurements down to ~30 nm lateral resolution. We correlated the subcellular localization of the chemotherapy drug cisplatin simultaneously with five subnuclear structures. Cisplatin was preferentially enriched in nuclear speckles and excluded from closed-chromatin regions, indicative of a role for cisplatin in active regions of chromatin. Unexpectedly, cells surviving multi-drug treatment with cisplatin and the BET inhibitor JQ1 demonstrated near total cisplatin exclusion from the nucleus, suggesting that selective subcellular drug relocalization may modulate resistance to this important chemotherapeutic treatment. Multiplexed high-resolution imaging techniques, such as HD-MIBI, will enable studies of biomolecules and drug distributions in biologically relevant subcellular microenvironments by visualizing the processes themselves in concert, rather than inferring mechanism through surrogate analyses. Multiplexed ion beam imaging can provide subcellular localisation information but with limited resolution. Here the authors report an ion beam imaging method with nanoscale resolution which they use to assess the subcellular distribution of cisplatin.