Showing papers by "Garry P. Nolan published in 2023"
TL;DR: In this article , the authors used the highly multiplexed immunofluorescence imaging technology CODEX to create a publicly browsable tissue atlas of inflammation in 42 tissue regions from 29 patients with UC and 5 healthy individuals.
Abstract: Although literature suggests that resistance to TNF inhibitor (TNFi) therapy in patients with ulcerative colitis (UC) is partially linked to immune cell populations in the inflamed region, there is still substantial uncertainty underlying the relevant spatial context. Here, we used the highly multiplexed immunofluorescence imaging technology CODEX to create a publicly browsable tissue atlas of inflammation in 42 tissue regions from 29 patients with UC and 5 healthy individuals. We analyzed 52 biomarkers on 1,710,973 spatially resolved single cells to determine cell types, cell-cell contacts, and cellular neighborhoods. We observed that cellular functional states are associated with cellular neighborhoods. We further observed that a subset of inflammatory cell types and cellular neighborhoods are present in patients with UC with TNFi treatment, potentially indicating resistant niches. Last, we explored applying convolutional neural networks (CNNs) to our dataset with respect to patient clinical variables. We note concerns and offer guidelines for reporting CNN-based predictions in similar datasets.
5 citations
TL;DR: In this article , a matching with partial overlap (MARIO) algorithm was proposed to account for both shared and distinct features, while consisting of vital filtering steps to avoid suboptimal matching.
Abstract: The ability to align individual cellular information from multiple experimental sources is fundamental for a systems-level understanding of biological processes. However, currently available tools are mainly designed for single-cell transcriptomics matching and integration, and generally rely on a large number of shared features across datasets for cell matching. This approach underperforms when applied to single-cell proteomic datasets due to the limited number of parameters simultaneously accessed and lack of shared markers across these experiments. Here, we introduce a cell-matching algorithm, matching with partial overlap (MARIO) that accounts for both shared and distinct features, while consisting of vital filtering steps to avoid suboptimal matching. MARIO accurately matches and integrates data from different single-cell proteomic and multimodal methods, including spatial techniques and has cross-species capabilities. MARIO robustly matched tissue macrophages identified from COVID-19 lung autopsies via codetection by indexing imaging to macrophages recovered from COVID-19 bronchoalveolar lavage fluid by cellular indexing of transcriptomes and epitopes by sequencing, revealing unique immune responses within the lung microenvironment of patients with COVID.
3 citations
TL;DR: In this article , Physioxia improves the selectivity of PVA-based mouse HSC cultures and deplete GvHD-causing T cells, which is beneficial for T cells.
2 citations
2 citations
TL;DR: MaxFuse as mentioned in this paper is a cross-modal data integration method that, through iterative co-embedding, data smoothing, and cell matching, leverages all information in each modality to obtain high-quality integration.
Abstract: single-cell sequencing methods have enabled the profiling of multiple types of molecular readouts at cellular resolution, and recent developments in spatial barcoding, in situ hybridization, and in situ sequencing allow such molecular readouts to retain their spatial context. Since no technology can provide complete characterization across all layers of biological modalities within the same cell, there is pervasive need for computational cross-modal integration (also called diagonal integration) of single-cell and spatial omics data. For current methods, the feasibility of cross-modal integration relies on the existence of highly correlated, a priori “linked” features. When such linked features are few or uninformative, a scenario that we call “weak linkage”, existing methods fail. We developed MaxFuse, a cross-modal data integration method that, through iterative co-embedding, data smoothing, and cell matching, leverages all information in each modality to obtain high-quality integration. MaxFuse is modality-agnostic and, through comprehensive benchmarks on single-cell and spatial ground-truth multiome datasets, demonstrates high robustness and accuracy in the weak linkage scenario. A prototypical example of weak linkage is the integration of spatial proteomic data with single-cell sequencing data. On two example analyses of this type, we demonstrate how MaxFuse enables the spatial consolidation of proteomic, transcriptomic and epigenomic information at single-cell resolution on the same tissue section.
2 citations
TL;DR: In this paper , a biochemical mechanism of action for glucose in modulating the dimerization and function of an RNA helicase essential for tissue differentiation was uncovered, which was associated with re-localization from the nucleolus to the nucleoplasm where DDX21 assembled into larger protein complexes containing RNA splicing factors.
1 citations
TL;DR: In this paper , the authors applied single-cell RNA sequencing (scRNA-seq) and spatial transcriptomics to both dissociated and intact, freshly isolated sinonasal human tissues to investigate the cellular and molecular heterogeneity of CRS with and without nasal polyp formation compared to non-CRS control samples.
Abstract: Chronic rhinosinusitis (CRS) is a common inflammatory disease of the sinonasal cavity that affects millions of individuals worldwide. The complex pathophysiology of CRS remains poorly understood, with emerging evidence implicating the orchestration between diverse immune and epithelial cell types in disease progression. We applied single-cell RNA sequencing (scRNA-seq) and spatial transcriptomics to both dissociated and intact, freshly isolated sinonasal human tissues to investigate the cellular and molecular heterogeneity of CRS with and without nasal polyp formation compared to non-CRS control samples. Our findings reveal a mechanism for macrophage-eosinophil recruitment into the nasal mucosa, systematic dysregulation of CD4+ and CD8+ T cells, and enrichment of mast cell populations to the upper airway tissues with intricate interactions between mast cells and CD4 T cells. Additionally, we identify immune-epithelial interactions and dysregulation, particularly involving understudied basal progenitor cells and Tuft chemosensory cells. We further describe a distinct basal cell differential trajectory in CRS patients with nasal polyps (NP), and link it to NP formation through immune-epithelial remodeling. By harnessing stringent patient tissue selection and advanced technologies, our study unveils novel aspects of CRS pathophysiology, and sheds light onto both intricate immune and epithelial cell interactions within the disrupted CRS tissue microenvironment and promising targets for therapeutic intervention. These findings expand upon existing knowledge of nasal inflammation and provide a comprehensive resource towards understanding the cellular and molecular mechanisms underlying this uniquely complex disease entity, and beyond.
TL;DR: Tidytof as discussed by the authors is an open-source R package for analyzing high-dimensional cytometry data using the increasingly popular "tidy data" interface, which is available under the MIT license.
Abstract: Abstract Summary While many algorithms for analyzing high-dimensional cytometry data have now been developed, the software implementations of these algorithms remain highly customized—this means that exploring a dataset requires users to learn unique, often poorly interoperable package syntaxes for each step of data processing. To solve this problem, we developed {tidytof}, an open-source R package for analyzing high-dimensional cytometry data using the increasingly popular ‘tidy data’ interface. Availability and implementation {tidytof} is available at https://github.com/keyes-timothy/tidytof and is released under the MIT license. It is supported on Linux, MS Windows and MacOS. Additional documentation is available at the package website (https://keyes-timothy.github.io/tidytof/). Supplementary information Supplementary data are available at Bioinformatics Advances online.
TL;DR: In this article, a comprehensive picture of the coordinated changes in epithelial, stromal and immune compartments during development of Barrett's-associated esophageal adenocarcinoma, patient-matched samples corresponding to various phases of disease progression were collected from 12 patients, each of which had at a given time point lesions at multiple stages progression (matched-normal, metaplasia, dysplasia and carcinoma).
Abstract: Esophageal adenocarcinoma arises from Barrett’s esophagus, a precancerous metaplastic replacement of squamous by columnar epithelium in response to chronic inflammation. Multi-omics profiling, integrating single-cell transcriptomics, extracellular matrix proteomics, tissue-mechanics and spatial proteomics of 64 samples from 12 patients’ paths of progression from squamous epithelium through metaplasia, dysplasia to adenocarcinoma, revealed shared and patient-specific progression characteristics. The classic metaplastic replacement of epithelial cells was paralleled by metaplastic changes in stromal cells, ECM and tissue stiffness. Strikingly, this change in tissue state at metaplasia was already accompanied by appearance of fibroblasts with characteristics of carcinoma-associated fibroblasts and of an NK cell-associated immunosuppressive microenvironment. Thus, Barrett’s esophagus progresses as a coordinated multi-component system, supporting treatment paradigms that go beyond targeting cancerous cells to incorporating stromal reprogramming. Graphical Abstract To obtain a comprehensive picture of the coordinated changes in epithelial, stromal and immune compartments during development of Barrett’s-associated esophageal adenocarcinoma, patient-matched samples corresponding to various phases of disease progression were collected from 12 patients, each of which had at a given time point lesions at multiple stages progression (matched-normal, metaplasia, dysplasia, and carcinoma). Matched “normal” gastric tissues were also collected. These sample were analyzed by single cell RNA-sequencing (scRNAseq) for single-cell resolution transcriptomics and Copy Number Variant (CNV), by proteomics for extracellular matrix (ECM) proteins, by Atomic Force Microscopy (AFM for tissue stiffness and by CODEX spatial proteomics imaging The integrative multi-omics analysis exposed drastic alterations in cell type composition and shifts in cell states in all three compartments. A large subpopulation of fibroblasts absent in the normal esophagus and characteristic of dysplasia and adenocarcinoma sample, that based on markers would indeed be considered cancer associated fibroblasts (CAF), appeared already in the metaplastic phase. This fibroblast subpopulation had transcriptomes virtually indistinguishable with fibroblasts of the cancer free gastric epithelium in these patients
TL;DR: In this article , the authors identify coordination between the glucocorticoid receptor pathway with B-cell developmental pathways, and they use Dasatinib to overcome GC resistance in acute lymphoblastic leukemia (BCP-ALL).
Abstract: Resistance to glucocorticoids (GC) is associated with an increased risk of relapse in B-cell progenitor acute lymphoblastic leukemia (BCP-ALL). Performing transcriptomic and single-cell proteomic studies in healthy B-cell progenitors, we herein identify coordination between the glucocorticoid receptor pathway with B-cell developmental pathways. Healthy pro-B cells most highly express the glucocorticoid receptor, and this developmental expression is conserved in primary BCP-ALL cells from patients at diagnosis and relapse. In-vitro and in vivo glucocorticoid treatment of primary BCP-ALL cells demonstrate that the interplay between B-cell development and the glucocorticoid pathways is crucial for GC resistance in leukemic cells. Gene set enrichment analysis in BCP-ALL cell lines surviving GC treatment show enrichment of B cell receptor signaling pathways. In addition, primary BCP-ALL cells surviving GC treatment in vitro and in vivo demonstrate a late pre-B cell phenotype with activation of PI3K/mTOR and CREB signaling. Dasatinib, a multi-kinase inhibitor, most effectively targets this active signaling in GC-resistant cells, and when combined with glucocorticoids, results in increased cell death in vitro and decreased leukemic burden and prolonged survival in an in vivo xenograft model. Targeting the active signaling through the addition of dasatinib may represent a therapeutic approach to overcome GC resistance in BCP-ALL.
TL;DR: In this paper , the authors discriminate, spatially resolve and reveal the function of five distinct macrophage niches within malignant and benign breast and colon tissue, and discover that IL4I1 macrophages populate niches with high cell turnover where they phagocytose dying cells.
Abstract: Summary Tumor-associated macrophages (TAMs) display heterogeneous phenotypes. Yet the exact tissue cues that shape macrophage functional diversity are incompletely understood. Here we discriminate, spatially resolve and reveal the function of five distinct macrophage niches within malignant and benign breast and colon tissue. We found that SPP1 TAMs reside in hypoxic and necrotic tumor regions, and a novel subset of FOLR2 tissue resident macrophages (TRMs) supports the plasma cell tissue niche. We discover that IL4I1 macrophages populate niches with high cell turnover where they phagocytose dying cells. Significantly, IL4I1 TAMs abundance correlates with anti-PD1 treatment response in breast cancer. Furthermore, NLRP3 inflammasome activation in NLRP3 TAMs correlates with neutrophil infiltration in the tumors and is associated with poor outcome in breast cancer patients. This suggests the NLRP3 inflammasome as a novel cancer immunetherapy target. Our work uncovers context-dependent roles of macrophage subsets, and suggests novel predictive markers and macrophage subset-specific therapy targets.
TL;DR: In this article , the authors examined pseudotemporalspatial patterns of insulitis and exocrine inflammation within large pancreatic tissue sections, and identified four sub-states of INSUIT characterized by CD8+T cells at different stages of activation.
Abstract: In autoimmune Type 1 diabetes (T1D), immune cells progressively infiltrate and destroy the islets of Langerhans, islands of endocrine tissue dispersed throughout the pancreas. However, it is unclear how this process, called insulitis, develops and progresses within this organ. Here, using highly multiplexed CO-Detection by indEXing (CODEX) tissue imaging and cadaveric pancreas samples from pre-T1D, T1D, and non-T1D donors, we examine pseudotemporalspatial patterns of insulitis and exocrine inflammation within large pancreatic tissue sections. We identify four sub-states of insulitis characterized by CD8+T cells at different stages of activation. We further find that exocrine compartments of pancreatic lobules affected by insulitis have distinct cellularity, suggesting that extra-islet factors may make particular lobules permissive to disease. Finally, we identify staging areas, immature tertiary lymphoid structures away from islets where CD8+T cells appear to assemble before they navigate to islets. Together, these data implicate the extra-islet pancreas in autoimmune insulitis, greatly expanding the boundaries of T1D pathogenesis.
TL;DR: Expand and comPRESS hydrOgels (ExPRESSO) as discussed by the authors is an ExM framework that allows high-plex protein staining, physical expansion, and removal of water, while retaining the lateral tissue expansion.
Abstract: Cellular organization and functions encompass multiple scales in vivo. Emerging high-plex imaging technologies are limited in resolving subcellular biomolecular features. Expansion Microscopy (ExM) and related techniques physically expand samples for enhanced spatial resolution, but are challenging to be combined with high-plex imaging technologies to enable integrative multiscaled tissue biology insights. Here, we introduce Expand and comPRESS hydrOgels (ExPRESSO), an ExM framework that allows high-plex protein staining, physical expansion, and removal of water, while retaining the lateral tissue expansion. We demonstrate ExPRESSO imaging of archival clinical tissue samples on Multiplexed Ion Beam Imaging and Imaging Mass Cytometry platforms, with detection capabilities of > 40 markers. Application of ExPRESSO on archival human lymphoid and brain tissues resolved tissue architecture at the subcellular level, particularly that of the blood-brain barrier. ExPRESSO hence provides a platform for extending the analysis compatibility of hydrogel-expanded biospecimens to mass spectrometry, with minimal modifications to protocols and instrumentation.
TL;DR: In this paper , a hypothesis-free graph Fourier transform model, SpaGFT, is presented to represent spatially organized features using the Fourier coefficients, leading to an accurate representation of spatially variable genes and proteins and the characterization of TM at a fast computational speed.
Abstract: Tissue module (TM) is a spatially organized tissue region and executes specialized biological functions, recurring and varying at different tissue sites. However, the computational identification of TMs poses challenges due to their convoluted biological functions, poorly-defined molecular features, and varying spatially organized patterns. Here, we present a hypothesis-free graph Fourier transform model, SpaGFT, to represent spatially organized features using the Fourier coefficients, leading to an accurate representation of spatially variable genes and proteins and the characterization of TM at a fast computational speed. We implemented sequencing-based and imaging-based spatial transcriptomics, spatial-CITE-seq, and spatial proteomics to identify spatially variable genes and proteins, define TM identities, and infer convoluted functions among TMs in mouse brains and human lymph nodes. We collected a human tonsil sample and performed CODEX to accurately demonstrate molecular and cellular variability within the secondary follicle structure. The superior accuracy, scalability, and interpretability of SpaGFT indicate that it is an effective representation of spatially-resolved omics data and an essential tool for bringing new insights into molecular tissue biology.
TL;DR: For example, MAPS (Machine learning for Analysis of Proteomics in Spatial biology) as discussed by the authors is a machine learning approach facilitating rapid and precise cell type identification with human-level accuracy from spatial proteomics data.
Abstract: Highly multiplexed protein imaging is emerging as a potent technique for analyzing protein distribution within cells and tissues in their native context. However, existing cell annotation methods utilizing high-plex spatial proteomics data are resource intensive and necessitate iterative expert input, thereby constraining their scalability and practicality for extensive datasets. We introduce MAPS (Machine learning for Analysis of Proteomics in Spatial biology), a machine learning approach facilitating rapid and precise cell type identification with human-level accuracy from spatial proteomics data. Validated on multiple in-house and publicly available MIBI and CODEX datasets, MAPS outperforms current annotation techniques in terms of speed and accuracy, achieving pathologist-level precision even for challenging cell types, including tumor cells of immune origin. By democratizing rapidly deployable and scalable machine learning annotation, MAPS holds significant potential to expedite advances in tissue biology and disease comprehension.