Showing papers by "Garry P. Nolan published in 2022"

CellSeg: a robust, pre-trained nucleus segmentation and pixel quantification software for highly multiplexed fluorescence images

Abstract: 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.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.CellSeg is a robust cell segmentation software for analyzing highly multiplexed tissue images, accessible to biology researchers of any programming skill level.

SARS-CoV-2 infection drives an inflammatory response in human adipose tissue through infection of adipocytes and macrophages

Abstract: Obesity, characterized by chronic low-grade inflammation of the adipose tissue, is associated with adverse coronavirus disease 2019 (COVID-19) outcomes, yet the underlying mechanism is unknown. To explore whether severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection of adipose tissue contributes to pathogenesis, we evaluated COVID-19 autopsy cases and deeply profiled the response of adipose tissue to SARS-CoV-2 infection in vitro. In COVID-19 autopsy cases, we identified SARS-CoV-2 RNA in adipocytes with an associated inflammatory infiltrate. We identified two distinct cellular targets of infection: adipocytes and a subset of inflammatory adipose tissue–resident macrophages. Mature adipocytes were permissive to SARS-CoV-2 infection; although macrophages were abortively infected, SARS-CoV-2 initiated inflammatory responses within both the infected macrophages and bystander preadipocytes. These data suggest that SARS-CoV-2 infection of adipose tissue could contribute to COVID-19 severity through replication of virus within adipocytes and through induction of local and systemic inflammation driven by infection of adipose tissue–resident macrophages. Description SARS-CoV-2 is capable of infecting human adipose tissue and drives inflammation. Infecting adipocytes Obesity is a known factor associated with COVID-19 severity. However, the precise mechanism by which obesity promotes disease severity is unknown. Here, Martínez-Colón et al. found that SARS-CoV-2, the virus that causes COVID-19, can productively infect mature adipocytes and abortively infect adipose tissue–resident macrophages. Infection of both cell types drove inflammatory responses, and the combination of viral replication and inflammation may help explain why obesity is associated with more severe COVID-19.

CellSeg: a robust, pre-trained nucleus segmentation and pixel quantification software for highly multiplexed fluorescence images

Abstract: 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.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.CellSeg is a robust cell segmentation software for analyzing highly multiplexed tissue images, accessible to biology researchers of any programming skill level.

A Comprehensive Atlas of Immunological Differences Between Humans, Mice, and Non-Human Primates

Abstract: Animal models are an integral part of the drug development and evaluation process. However, they are unsurprisingly imperfect reflections of humans, and the extent and nature of many immunological differences are unknown. With the rise of targeted and biological therapeutics, it is increasingly important that we understand the molecular differences in the immunological behavior of humans and model organisms. However, very few antibodies are raised against non-human primate antigens, and databases of cross-reactivity between species are incomplete. Thus, we screened 332 antibodies in five immune cell populations in blood from humans and four non-human primate species generating a comprehensive cross-reactivity catalog that includes cell type-specificity. We used this catalog to create large mass cytometry universal cross-species phenotyping and signaling panels for humans, along with three of the model organisms most similar to humans: rhesus and cynomolgus macaques and African green monkeys; and one of the mammalian models most widely used in drug development: C57BL/6 mice. As a proof-of-principle, we measured immune cell signaling responses across all five species to an array of 15 stimuli using mass cytometry. We found numerous instances of different cellular phenotypes and immune signaling events occurring within and between species, and detailed three examples (double-positive T cell frequency and signaling; granulocyte response to Bacillus anthracis antigen; and B cell subsets). We also explore the correlation of herpes simian B virus serostatus on the immune profile. Antibody panels and the full dataset generated are available online as a resource to enable future studies comparing immune responses across species during the evaluation of therapeutics.

Identification of cell types in multiplexed in situ images by combining protein expression and spatial information using CELESTA

Abstract: Advances in multiplexed in situ imaging are revealing important insights in spatial biology. However, cell type identification remains a major challenge in imaging analysis, with most existing methods involving substantial manual assessment and subjective decisions for thousands of cells. We developed an unsupervised machine learning algorithm, CELESTA, which identifies the cell type of each cell, individually, using the cell’s marker expression profile and, when needed, its spatial information. We demonstrate the performance of CELESTA on multiplexed immunofluorescence images of colorectal cancer and head and neck squamous cell carcinoma (HNSCC). Using the cell types identified by CELESTA, we identify tissue architecture associated with lymph node metastasis in HNSCC, and validate our findings in an independent cohort. By coupling our spatial analysis with single-cell RNA-sequencing data on proximal sections of the same specimens, we identify cell–cell crosstalk associated with lymph node metastasis, demonstrating the power of CELESTA to facilitate identification of clinically relevant interactions. CELESTA identifies cell types in multiplexed imaging datasets based on the expression profiles of cells and their spatial information.

Identification of cell types in multiplexed in situ images by combining protein expression and spatial information using CELESTA

Abstract: Advances in multiplexed in situ imaging are revealing important insights in spatial biology. However, cell type identification remains a major challenge in imaging analysis, with most existing methods involving substantial manual assessment and subjective decisions for thousands of cells. We developed an unsupervised machine learning algorithm, CELESTA, which identifies the cell type of each cell, individually, using the cell’s marker expression profile and, when needed, its spatial information. We demonstrate the performance of CELESTA on multiplexed immunofluorescence images of colorectal cancer and head and neck squamous cell carcinoma (HNSCC). Using the cell types identified by CELESTA, we identify tissue architecture associated with lymph node metastasis in HNSCC, and validate our findings in an independent cohort. By coupling our spatial analysis with single-cell RNA-sequencing data on proximal sections of the same specimens, we identify cell–cell crosstalk associated with lymph node metastasis, demonstrating the power of CELESTA to facilitate identification of clinically relevant interactions. CELESTA identifies cell types in multiplexed imaging datasets based on the expression profiles of cells and their spatial information.

Immunotherapy of glioblastoma explants induces interferon-γ responses and spatial immune cell rearrangements in tumor center, but not periphery

Abstract: 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.

Integrated plasma proteomic and single-cell immune signaling network signatures demarcate mild, moderate, and severe COVID-19

Abstract: The biological determinants underlying the range of coronavirus 2019 (COVID-19) clinical manifestations are not fully understood. Here, over 1,400 plasma proteins and 2,600 single-cell immune features comprising cell phenotype, endogenous signaling activity, and signaling responses to inflammatory ligands are cross-sectionally assessed in peripheral blood from 97 patients with mild, moderate, and severe COVID-19 and 40 uninfected patients. Using an integrated computational approach to analyze the combined plasma and single-cell proteomic data, we identify and independently validate a multi-variate model classifying COVID-19 severity (multi-class area under the curve [AUC]training = 0.799, p = 4.2e-6; multi-class AUCvalidation = 0.773, p = 7.7e-6). Examination of informative model features reveals biological signatures of COVID-19 severity, including the dysregulation of JAK/STAT, MAPK/mTOR, and nuclear factor κB (NF-κB) immune signaling networks in addition to recapitulating known hallmarks of COVID-19. These results provide a set of early determinants of COVID-19 severity that may point to therapeutic targets for prevention and/or treatment of COVID-19 progression.

Annotation of spatially resolved single-cell data with STELLAR

Abstract: Accurate cell-type annotation from spatially resolved single cells is crucial to understand functional spatial biology that is the basis of tissue organization. However, current computational methods for annotating spatially resolved single-cell data are typically based on techniques established for dissociated single-cell technologies and thus do not take spatial organization into account. Here we present STELLAR, a geometric deep learning method for cell-type discovery and identification in spatially resolved single-cell datasets. STELLAR automatically assigns cells to cell types present in the annotated reference dataset and discovers novel cell types and cell states. STELLAR transfers annotations across different dissection regions, different tissues and different donors, and learns cell representations that capture higher-order tissue structures. We successfully applied STELLAR to CODEX multiplexed fluorescent microscopy data and multiplexed RNA imaging datasets. Within the Human BioMolecular Atlas Program, STELLAR has annotated 2.6 million spatially resolved single cells with dramatic time savings.

A single cell spatial temporal atlas of skeletal muscle reveals cellular neighborhoods that orchestrate regeneration and become disrupted in aging

Abstract: Our mobility requires muscle regeneration throughout life. Yet our knowledge of the interplay of cell types required to rebuild injured muscle is lacking, because most single cell assays require tissue dissociation. Here we use multiplexed spatial proteomics and neural network analyses to resolve a single cell spatiotemporal atlas of 34 cell types during muscle regeneration and aging. This atlas maps interactions of immune, fibrogenic, vascular, nerve, and myogenic cells at sites of injury in relation to tissue architecture and extracellular matrix. Spatial pseudotime mapping reveals sequential cellular neighborhoods that mediate repair and a nodal role for immune cells. We confirm this role by macrophage depletion, which triggers formation of aberrant neighborhoods that obstruct repair. In aging, immune dysregulation is chronic, cellular neighborhoods are disrupted, and an autoimmune response is evident at sites of denervation. Our findings highlight the spatial cellular ecosystem that orchestrates muscle regeneration, and is altered in aging. Highlights Single cell resolution spatial atlas resolves a cellular ecosystem of 34 cell types in multicellular neighborhoods that mediate efficient skeletal muscle repair Highly multiplexed spatial proteomics, neural network and machine learning uncovers temporal dynamics in the spatial crosstalk between immune, fibrogenic, vascular, nerve, and muscle stem cells and myofibers during regeneration Spatial pseudotime mapping reveals coherent formation of multicellular neighborhoods during efficacious repair and the nodal role of immune cells in coordinating muscle repair In aged muscle, cellular neighborhoods are disrupted by a chronically inflamed state and autoimmunity

One-Way Matching of Datasets with Low Rank Signals

Abstract: We study one-way matching of a pair of datasets with low rank signals. Under a stylized model, we first derive information-theoretic limits of matching. We then show that linear assignment with projected data achieves fast rates of convergence and sometimes even minimax rate optimality for this task. The theoretical error bounds are corroborated by simulated examples. Furthermore, we illustrate practical use of the matching procedure on two single-cell data examples.

Postmitotic G1 phase survivin drives mitogen-independent cell division of B lymphocytes

Abstract: Significance The prevailing dogma is that renewed mitogenic signaling is essential to traverse G1 phase of the cell cycle after each division. B lymphocytes undergo multiple mitotic divisions, termed clonal expansion, to expand antigen-specific cells that mediate effective immunity. Here we demonstrate that B cells that have undergone one cell division continue to proliferate even in absence of further mitogenic signals. This mitogen-independent proliferation is accompanied by an altered G1 phase marked by transcriptomic and proteomic features of G2/M. Survivin, a G2/M-specific oncogene, is required in G1 to achieve mitogen-independent proliferation.

Variation of Immune Cell Responses in Humans Reveals Sex-Specific Coordinated Signaling Across Cell Types

Abstract: Assessing the health and competence of the immune system is central to evaluating vaccination responses, autoimmune conditions, cancer prognosis, and treatment. With an increasing number of studies examining immune dysregulation, there is a growing need for a curated reference of variation in immune parameters in healthy individuals. We used mass cytometry (CyTOF) to profile blood from 86 humans in response to 15 ex vivo immune stimuli. We present reference ranges for cell-specific immune markers and highlight differences that appear across sex and age. We identified modules of immune features that suggest there exists an underlying structure to the immune system based on signaling pathway responses across cell types. We observed increased MAPK signaling in inflammatory pathways in innate immune cells and greater overall coordination of immune cell responses in females. In contrast, males exhibited stronger pSTAT1 and pTBK1 responses. These reference data are publicly available as a resource for immune profiling studies.

Highly multiplexed spatial profiling with CODEX: bioinformatic analysis and application in human disease

Abstract: Multiplexed imaging, which enables spatial localization of proteins and RNA to cells within tissues, complements existing multi-omic technologies and has deepened our understanding of health and disease. CODEX, a multiplexed single-cell imaging technology, utilizes a microfluidics system that incorporates DNA barcoded antibodies to visualize 50 + cellular markers at the single-cell level. Here, we discuss the latest applications of CODEX to studies of cancer, autoimmunity, and infection as well as current bioinformatics approaches for analysis of multiplexed imaging data from preprocessing to cell segmentation and marker quantification to spatial analysis techniques. We conclude with a commentary on the challenges and future developments for multiplexed spatial profiling.

A spatial map of human macrophage niches links tissue location with function

Abstract: Macrophages are the most abundant immune cell type in the tumor microenvironment (TME). Yet the spatial distribution and cell interactions that shape macrophage function are incompletely understood. Here we use single-cell RNA sequencing data and multiplex imaging to discriminate and spatially resolve macrophage niches within benign and malignant breast and colon tissue. We discover four distinct tissue-resident macrophage (TRM) layers within benign bowel, two TRM niches within benign breast, and three tumor-associated macrophage (TAM) populations within breast and colon cancer. We demonstrate that IL4I1 marks phagocytosing macrophages, SPP1 TAMs are enriched in hypoxic and necrotic tumor regions, and a novel subset of FOLR2 TRMs localizes within the plasma cell niche. Furthermore, NLRP3 TAMs that colocalize with neutrophils activate an inflammasome in the TME and in Crohn’s disease and are associated with poor outcomes in breast cancer patients. This work suggests novel macrophage therapy targets and provides a framework to study human macrophage function in clinical samples.

A real-time GPU-accelerated parallelized image processor for large-scale multiplexed fluorescence microscopy data

Abstract: Highly multiplexed, single-cell imaging has revolutionized our understanding of spatial cellular interactions associated with health and disease. With ever-increasing numbers of antigens, region sizes, and sample sizes, multiplexed fluorescence imaging experiments routinely produce terabytes of data. Fast and accurate processing of these large-scale, high-dimensional imaging data is essential to ensure reliable segmentation and identification of cell types and for characterization of cellular neighborhoods and inference of mechanistic insights. Here, we describe RAPID, a Real-time, GPU-Accelerated Parallelized Image processing software for large-scale multiplexed fluorescence microscopy Data. RAPID deconvolves large-scale, high-dimensional fluorescence imaging data, stitches and registers images with axial and lateral drift correction, and minimizes tissue autofluorescence such as that introduced by erythrocytes. Incorporation of an open source CUDA-driven, GPU-assisted deconvolution produced results similar to fee-based commercial software. RAPID reduces data processing time and artifacts and improves image contrast and signal-to-noise compared to our previous image processing pipeline, thus providing a useful tool for accurate and robust analysis of large-scale, multiplexed, fluorescence imaging data.

Subcortical Brain Morphometry Differences between Adults with Autism Spectrum Disorder and Schizophrenia

Abstract: Autism spectrum disorder (ASD) and schizophrenia (SZ) are neuropsychiatric disorders that overlap in symptoms associated with social-cognitive impairment. Subcortical structures play a significant role in cognitive and social-emotional behaviors and their abnormalities are associated with neuropsychiatric conditions. This exploratory study utilized ABIDE II/COBRE MRI and corresponding phenotypic datasets to compare subcortical volumes of adults with ASD (n = 29), SZ (n = 51) and age and gender matched neurotypicals (NT). We examined the association between subcortical volumes and select behavioral measures to determine whether core symptomatology of disorders could be explained by subcortical association patterns. We observed volume differences in ASD (viz., left pallidum, left thalamus, left accumbens, right amygdala) but not in SZ compared to their respective NT controls, reflecting morphometric changes specific to one of the disorder groups. However, left hippocampus and amygdala volumes were implicated in both disorders. A disorder-specific negative correlation (r = −0.39, p = 0.038) was found between left-amygdala and scores on the Social Responsiveness Scale (SRS) Social-Cognition in ASD, and a positive association (r = 0.29, p = 0.039) between full scale IQ (FIQ) and right caudate in SZ. Significant correlations between behavior measures and subcortical volumes were observed in NT groups (ASD-NT range; r = −0.53 to −0.52, p = 0.002 to 0.004, SZ-NT range; r = −0.41 to −0.32, p = 0.007 to 0.021) that were non-significant in the disorder groups. The overlap of subcortical volumes implicated in ASD and SZ may reflect common neurological mechanisms. Furthermore, the difference in correlation patterns between disorder and NT groups may suggest dysfunctional connectivity with cascading effects unique to each disorder and a potential role for IQ in mediating behavior and brain circuits.

Multimodal and spatially resolved profiling identifies distinct patterns of T-cell infiltration in nodal B-cell lymphoma entities

Abstract: Summary T-cell-engaging immunotherapies have improved the treatment of nodal B-cell lymphoma, but responses vary highly. Future improvements of such therapies require better understanding of the variety of lymphoma-infiltrating T-cells. We employed single-cell RNA and T-cell receptor sequencing alongside quantification of surface proteins, flow cytometry and multiplexed immunofluorescence on 101 lymph nodes from healthy controls, and patients with diffuse large B-cell, mantle cell, follicular, or marginal zone lymphoma. This multimodal resource revealed entity-specific quantitative and spatial aberrations of the T-cell microenvironment. Clonal PD1+ TCF7− but not PD1+ TCF7+ cytotoxic T-cells converged into terminally exhausted T-cells, the proportions of which were variable across entities and linked to inferior prognosis. In follicular and marginal zone lymphoma, we observed expansion of follicular helper and IKZF3+ regulatory T-cells, which were clonally related and inversely associated with tumor grading. Overall, we portray lymphoma-infiltrating T-cells with unprecedented comprehensiveness and decipher both beneficial and adverse dimensions of T-cell response.

Predictive models demonstrate age‐dependent association of subcortical volumes and cognitive measures

Abstract: Whether brain matter volume is correlated with cognitive functioning and higher intelligence is controversial. We explored this relationship by analysis of data collected on 193 healthy young and older adults through the “Leipzig Study for Mind–Body–Emotion Interactions” (LEMON) study. Our analysis involved four cognitive measures: fluid intelligence, crystallized intelligence, cognitive flexibility, and working memory. Brain subregion volumes were determined by magnetic resonance imaging. We normalized each subregion volume to the estimated total intracranial volume and conducted training simulations to compare the predictive power of normalized volumes of large regions of the brain (i.e., gray matter, cortical white matter, and cerebrospinal fluid), normalized subcortical volumes, and combined normalized volumes of large brain regions and normalized subcortical volumes. Statistical tests showed significant differences in the performance accuracy and feature importance of the subregion volumes in predicting cognitive skills for young and older adults. Random forest feature selection analysis showed that cortical white matter was the key feature in predicting fluid intelligence in both young and older adults. In young adults, crystallized intelligence was best predicted by caudate nucleus, thalamus, pallidum, and nucleus accumbens volumes, whereas putamen, amygdala, nucleus accumbens, and hippocampus volumes were selected for older adults. Cognitive flexibility was best predicted by the caudate, nucleus accumbens, and hippocampus in young adults and caudate and amygdala in older adults. Finally, working memory was best predicted by the putamen, pallidum, and nucleus accumbens in the younger group, whereas amygdala and hippocampus volumes were predictive in the older group. Thus, machine learning predictive models demonstrated an age‐dependent association between subcortical volumes and cognitive measures. These approaches may be useful in predicting the likelihood of age‐related cognitive decline and in testing of approaches for targeted improvement of cognitive functioning in older adults.

Aldehyde dehydrogenase 3A1 deficiency leads to mitochondrial dysfunction and impacts salivary gland stem cell phenotype

Abstract: Abstract Adult salivary stem/progenitor cells (SSPC) have an intrinsic property to self-renew in order to maintain tissue architecture and homeostasis. Adult salivary glands have been documented to harbor SSPC, which have been shown to play a vital role in the regeneration of the glandular structures postradiation damage. We have previously demonstrated that activation of aldehyde dehydrogenase 3A1 (ALDH3A1) after radiation reduced aldehyde accumulation in SSPC, leading to less apoptosis and improved salivary function. We subsequently found that sustained pharmacological ALDH3A1 activation is critical to enhance regeneration of murine submandibular gland after radiation damage. Further investigation shows that ALDH3A1 function is crucial for SSPC self-renewal and survival even in the absence of radiation stress. Salivary glands from Aldh3a1–/– mice have fewer acinar structures than wildtype mice. ALDH3A1 deletion or pharmacological inhibition in SSPC leads to a decrease in mitochondrial DNA copy number, lower expression of mitochondrial specific genes and proteins, structural abnormalities, lower membrane potential, and reduced cellular respiration. Loss or inhibition of ALDH3A1 also elevates ROS levels, depletes glutathione pool, and accumulates ALDH3A1 substrate 4-hydroxynonenal (4-HNE, a lipid peroxidation product), leading to decreased survival of murine SSPC that can be rescued by treatment with 4-HNE specific carbonyl scavengers. Our data indicate that ALDH3A1 activity protects mitochondrial function and is important for the regeneration activity of SSPC. This knowledge will help to guide our translational strategy of applying ALDH3A1 activators in the clinic to prevent radiation-related hyposalivation in head and neck cancer patients.

Overcoming barriers to solid tumor immunotherapy using natural killer cell therapies designed to mimic intraepithelial group 1 innate lymphoid cells

Abstract: Our work aims to design cell-based immunotherapies for maximum solid tumor efficacy by leveraging insights from tissue-resident cells and single-cell RNA sequencing of innate immune cell subsets present within patient tumors. We discovered that by co-culturing peripheral blood natural killer cells (pbNKs) with irradiated epithelial tumor cells, we induced high expression of surface integrins, increased cytotoxicity and IFNg production, decreased sensitivity to TGFb, and significantly enhanced solid tumoroid infiltration. The cells were profiled using CyTOF and we determined that they closely resembled intraepithelial group 1 innate lymphoid cells, so we refer to them as ieILC1-like cells. We quantified the cytotoxicity of ieILC1-like cells against a variety of target cell lines and determined that they are broadly cytotoxic and capable of antibody-dependent cellular cytotoxicity, which was assessed using cetuximab. Tumor-infiltrating capacity was modeled using 3D tumoroids grown from single-cell suspensions of epithelial tumor cell lines in basement membrane extracts; ieILC1-like NK cells or pbNK cells were fluorescently labeled, added to tumoroids, and imaged using confocal microscopy. We stimulated the cells in the presence or absence of overnight TGFb exposure to determine the extent of TGFb-mediated immunosuppression. ieILC1-like cells were more resistant to TGFb and produced significantly more IFNg after stimulation than their pbNK counterparts. Preliminary in vivo work indicates that ieILC1-like cells perform comparably to K562-expanded NK cells. Thus, ieILC1-like NK cells represent a novel class of cell therapy that are more capable of infiltrating solid tumors and resisting their immunosuppressive cues. Supported by NIH R35DE030054.

Abstract PD6-03: Spatio-molecular dissection of the breast cancer metastatic microenvironment

Abstract: Metastatic breast cancer (MBC) remains incurable due to inevitable development of therapeutic resistance. Although tumor cell intrinsic mechanisms of resistance in MBC are beginning to be elucidated by bulk sequencing studies, the roles of the tumor microenvironment and intratumor heterogeneity in therapeutic resistance remain underexplored due to both technological barriers and limited availability of samples. To comprehensively capture these characteristics we have adapted a research biopsy protocol to collect tissue for an array of single-cell and spatio-molecular assays whose performance we have optimized for MBC, including single-cell and single-nucleus RNA sequencing, Slide-Seq, Multiplexed Error-Robust FISH (MERFISH), Expansion Sequencing (ExSEQ), Co-detection by Indexing (CODEX) and Multiplexed Ion Beam Imaging (MIBI). To date, we have successfully performed single-cell or single-nucleus RNAseq in 67 MBC biopsies and generated detailed accompanying clinical annotations for each. These samples provide a representation of the clinicopathological diversity of MBC including different breast cancer subtypes (44 HR+/HER2-, 3 HR-/HER2+, 3 HR+/HER2+, 16 TNBC, 1 unknown), common anatomic sites of metastasis (37 liver, 9 axilla, 7 breast, 5 bone, 3 chest wall, 3 neck, 1 brain, 1 lung, 1 skin), metastatic presentations (53 recurrent, 14 de novo) and histologic subtypes in the breast (45 IDC, 7 ILC, 6 mixed, 3 DCIS, 1 mucinous, 5 unknown/NA). Following optimization, both single-cell and single-nucleus RNA seq perform well in these MBC biopsies recovering all expected cell types including the malignant, stromal (e.g. fibroblasts, endothelial cells), myeloid (e.g. monocytes, macrophages) and lymphoid compartments (e.g. T cells, B cells, NK cells) as well as relevant oncogenic programs (e.g. cell cycle programs in all compartments; EMT-like and ER signaling programs in the malignant compartment, immune checkpoint programs in the lymphoid compartment; and fibroblast activation and vascular homeostasis programs in the stromal compartment). In addition to differences between the two techniques, these data demonstrate substantial intratumor heterogeneity in cell type composition. For example in liver biopsies the average number of cells per sample compartment by single nucleus RNA-seq was 6745 malignant (56%, SD 4216), 4637 stromal (41%, SD 3727), 1196 lymphoid (8%, SD 1617) and 874 myeloid (6%, SD 852); in breast biopsies the average number of cells per compartment by single nucleus RNA-seq was 6421 malignant (70%, SD 3497), 1628 stromal (24%, SD 117), 333 lymphoid (4%, SD 170) and 213 myeloid (3%, SD 117). Additionally, we find both inter- and intra-tumor heterogeneity in expression patterns and programs including, for example, expression of ER, PR and HER2 within clinical receptor subtypes (log normalized counts for ER expression in tumor cells by single cell RNA-seq: HR+/HER2- 0.921 (SD 0.714); HR+/HER2+ 0.768 (SD 0.624); HR-/HER2+ 0.018 (SD 0.122); and HR-/HER2- 0.005 (SD 0.066). For a subset of 13 biopsies we are also completing the spatiomolecular characterization methods on serial sections of a single adjacent biopsy. This unique experimental setup was designed to enable efficient comparison and integration of these assays. In spite of differences between experimental techniques and readouts, cell typing can be approached by annotation transfer from matching single cell or single nucleus RNAseq data, enabling exploratory analyses including evaluation of spatial phenotypes and cell type colocalization. Overall, these single cell and spatial data afford a comprehensive atlas including cell types, cell states/programs, cell interactions and spatial organization in MBC lesions. Future analyses will include serial biopsies over time and integration of clinicopathologic data including therapeutic response and resistance. Citation Format: Daniel L Abravanel, Johanna Klughammer, Timothy Blosser, Yury Goltsev, Sizun Jiang, Yunjao Bai, Evan Murray, Shahar Alon, Yi Cui, Daniel R Goodwin, Anubhav Sinha, Ofir Cohen, Michal Slyper, Orr Ashenberg, Danielle Dionne, Judit Jané-Valbuena, Caroline BM Porter, Asa Segerstolpe, Julia Waldman, Sébastien Vigneau, Karla Helvie, Allison Frangieh, Laura DelloStritto, Miraj Patel, Jingyi We, Kathleen Pfaff, Nicole Cullen, Ana Lako, Madison Turner, Isaac Wakiro, Sara Napolitano, Abhay Kanodia, Rebecca Ortiz, Colin MacKichan, Stephanie Inga, Judy Chen, Aaron R Thorner, Asaf Rotem, Scott Rodig, Fei Chen, Edward S Boyden, Garry P Nolan, Xiaowei Zhuang, Orit Rozenblatt-Rosen, Bruce E Johnson, Aviv Regev, Nikhil Wagle. Spatio-molecular dissection of the breast cancer metastatic microenvironment [abstract]. In: Proceedings of the 2021 San Antonio Breast Cancer Symposium; 2021 Dec 7-10; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2022;82(4 Suppl):Abstract nr PD6-03.