Single-cell transcriptomic profiling maps monocyte/macrophage transitions after myocardial infarction in mice

AbstractRationaleMonocytes and macrophages have a critical and dual role in post-ischemic cardiac repair, as they can foster both tissue healing and damage. To decipher how monocytes/macrophages acquire heterogeneous functional phenotypes in the ischemic myocardium, we profiled the gene expression dynamics at the single-cell level in circulating and cardiac monocytes/macrophages following experimental myocardial infarction (MI) in mice.Methods and resultsUsing time-series single-cell transcriptome and cell surface epitope analysis of blood and cardiac monocytes/macrophages, as well as the integration of publicly available and independently generated single-cell RNA-seq data, we tracked the transitions in circulating and cardiac monocyte/macrophage states from homeostatic conditions up to 11 days after MI in mice. We show that MI induces marked and rapid transitions in the cardiac mononuclear phagocyte population, with almost complete disappearance of tissue resident macrophages 1 day after ischemia, and rapid infiltration of monocytes that locally acquire discrete and time-dependent transcriptional states within 3 to 7 days. Ischemic injury induced a shift of circulating monocytes towards granulocyte-like transcriptional features (Chil3, Lcn2, Prtn3). Trajectory inference analysis indicated that while conversion to Ly6Clow monocytes appears as the default fate of Ly6Chi monocytes in the blood, infiltrated monocytes acquired diverse gene expression signatures in the injured heart, notably transitioning to two main MI-associated macrophage populations characterized by MHCIIhi and Trem2hiIgf1hi gene expression signatures. Minor ischemia-associated macrophage populations with discrete gene expression signature suggesting specialized functions in e.g. iron handling or lipid metabolism were also observed. We further identified putative transcriptional regulators and new cell surface markers of cardiac monocyte/macrophage states.ConclusionsAltogether, our work provides a comprehensive landscape of circulating and cardiac monocyte/macrophage states and their regulators after MI, and will help to further understand their contribution to post-myocardial infarction heart repair.

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Evaluation of methods to assign cell type labels to cell clusters from single-cell RNAsequencing data

10.1101/562082 ◽

2019 ◽

Cited By ~ 4

Author(s):

J. Javier Díaz-Mejía ◽

Elaine C. Meng ◽

Alexander R. Pico ◽

Sonya A. MacParland ◽

Troy Ketela ◽

...

Keyword(s):

Gene Expression ◽

Single Cell ◽

Mononuclear Cells ◽

Characteristic Curve ◽

Gene Expression Signature ◽

Cell Type ◽

Cell Clusters ◽

Reference Cell ◽

Gene Expression Signatures ◽

Type Gene

AbstractIdentification of cell type subpopulations from complex cell mixtures using single-cell RNA-sequencing (scRNA-seq) data includes automated computational steps like data normalization, dimensionality reduction and cell clustering. However, assigning cell type labels to cell clusters is still conducted manually by most researchers, resulting in limited documentation, low reproducibility and uncontrolled vocabularies. Two bottlenecks to automating this task are the scarcity of reference cell type gene expression signatures and that some dedicated methods are available only as web servers with limited cell type gene expression signatures. In this study, we benchmarked four methods (CIBERSORT, GSEA, GSVA, and ORA) for the task of assigning cell type labels to cell clusters from scRNA-seq data. We used scRNA-seq datasets from liver, peripheral blood mononuclear cells and retinal neurons for which reference cell type gene expression signatures were available. Our results show that, in general, all four methods show a high performance in the task as evaluated by Receiver Operating Characteristic curve analysis (average AUC = 0.94, sd = 0.036), whereas Precision-Recall curve analyses show a wide variation depending on the method and dataset (average AUC = 0.53, sd = 0.24). CIBERSORT and GSVA were the top two performers. Additionally, GSVA was the fastest of the four methods and was more robust in cell type gene expression signature subsampling simulations. We provide an extensible framework to evaluate other methods and datasets at https://github.com/jdime/scRNAseq_cell_cluster_labeling.

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Evaluation of methods to assign cell type labels to cell clusters from single-cell RNA-sequencing data

F1000Research ◽

10.12688/f1000research.18490.1 ◽

2019 ◽

Vol 8 ◽

pp. 296 ◽

Cited By ~ 7

Author(s):

J. Javier Diaz-Mejia ◽

Elaine C. Meng ◽

Alexander R. Pico ◽

Sonya A. MacParland ◽

Troy Ketela ◽

...

Keyword(s):

Gene Expression ◽

Single Cell ◽

Rna Sequencing ◽

Gene Expression Signature ◽

Cell Type ◽

Cell Clusters ◽

Reference Cell ◽

Single Cell Rna Sequencing ◽

Gene Expression Signatures ◽

Type Gene

Background: Identification of cell type subpopulations from complex cell mixtures using single-cell RNA-sequencing (scRNA-seq) data includes automated computational steps like data normalization, dimensionality reduction and cell clustering. However, assigning cell type labels to cell clusters is still conducted manually by most researchers, resulting in limited documentation, low reproducibility and uncontrolled vocabularies. Two bottlenecks to automating this task are the scarcity of reference cell type gene expression signatures and the fact that some dedicated methods are available only as web servers with limited cell type gene expression signatures. Methods: In this study, we benchmarked four methods (CIBERSORT, GSEA, GSVA, and ORA) for the task of assigning cell type labels to cell clusters from scRNA-seq data. We used scRNA-seq datasets from liver, peripheral blood mononuclear cells and retinal neurons for which reference cell type gene expression signatures were available. Results: Our results show that, in general, all four methods show a high performance in the task as evaluated by receiver operating characteristic curve analysis (average area under the curve (AUC) = 0.94, sd = 0.036), whereas precision-recall curve analyses show a wide variation depending on the method and dataset (average AUC = 0.53, sd = 0.24). Conclusions: CIBERSORT and GSVA were the top two performers. Additionally, GSVA was the fastest of the four methods and was more robust in cell type gene expression signature subsampling simulations. We provide an extensible framework to evaluate other methods and datasets at https://github.com/jdime/scRNAseq_cell_cluster_labeling.

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Dynamics of Cardiac Neutrophil Diversity in Murine Myocardial Infarction

Circulation Research ◽

10.1161/circresaha.120.317200 ◽

2020 ◽

Vol 127 (9) ◽

Cited By ~ 1

Author(s):

Ehsan Vafadarnejad ◽

Giuseppe Rizzo ◽

Laura Krampert ◽

Panagiota Arampatzi ◽

Anahi-Paula Arias-Loza ◽

...

Keyword(s):

Gene Expression ◽

Myocardial Infarction ◽

Bone Marrow ◽

Single Cell ◽

Ischemic Heart ◽

Time Resolved ◽

Tissue Healing ◽

Receptor Interactions ◽

Blood Neutrophils ◽

Gene Expression Dynamics

Rationale: After myocardial infarction, neutrophils rapidly and massively infiltrate the heart, where they promote both tissue healing and damage. Objective: To characterize the dynamics of circulating and cardiac neutrophil diversity after infarction. Methods and results: We employed single-cell transcriptomics combined with cell surface epitope detection by sequencing to investigate temporal neutrophil diversity in the blood and heart after murine myocardial infarction. At day 1, 3, and 5 after infarction, cardiac Ly6G + (lymphocyte antigen 6G) neutrophils could be delineated into 6 distinct clusters with specific time-dependent patterning and proportions. At day 1, neutrophils were characterized by a gene expression profile proximal to bone marrow neutrophils ( Cd177 , Lcn2 , Fpr1 ), and putative activity of transcriptional regulators involved in hypoxic response ( Hif1a ) and emergency granulopoiesis ( Cebpb ). At 3 and 5 days, 2 major subsets of Siglecf hi (enriched for eg, Icam1 and Tnf ) and Siglecf low ( Slpi, Ifitm1 ) neutrophils were found. Cellular indexing of transcriptomes and epitopes by sequencing (CITE-seq) analysis in blood and heart revealed that while circulating neutrophils undergo a process of aging characterized by loss of surface CD62L and upregulation of Cxcr4 , heart infiltrating neutrophils acquired a unique SiglecF hi signature. SiglecF hi neutrophils were absent from the bone marrow and spleen, indicating local acquisition of the SiglecF hi signature. Reducing the influx of blood neutrophils by anti-Ly6G treatment increased proportions of cardiac SiglecF hi neutrophils, suggesting accumulation of locally aged neutrophils. Computational analysis of ligand/receptor interactions revealed putative pathways mediating neutrophil to macrophage communication in the myocardium. Finally, SiglecF hi neutrophils were also found in atherosclerotic vessels, revealing that they arise across distinct contexts of cardiovascular inflammation. Conclusions: Altogether, our data provide a time-resolved census of neutrophil diversity and gene expression dynamics in the mouse blood and ischemic heart at the single-cell level, and reveal a process of local tissue specification of neutrophils in the ischemic heart characterized by the acquisition of a SiglecF hi signature.

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Highly Multiplexed Cell Surface Immunophenotyping with Genotyping and Concurrent Transcriptomic Analysis of NPM1 mutated Acute Myeloid Leukemia

Blood ◽

10.1182/blood-2021-151366 ◽

2021 ◽

Vol 138 (Supplement 1) ◽

pp. 1288-1288

Author(s):

Devdeep Mukherjee ◽

Gege Gui ◽

Laura W. Dillon ◽

Christopher S. Hourigan

Keyword(s):

Gene Expression ◽

Acute Myeloid Leukemia ◽

Cell Surface ◽

Single Cell ◽

Myeloid Leukemia ◽

Type A ◽

Patient Specific ◽

Malignant Cells ◽

Surface Proteome ◽

Acute Myeloid

Abstract BACKGROUND: The pathogenesis of acute myeloid leukemia (AML) is often attributed to the presence of somatic allelic variant(s) in hematopoietic stem/progenitor cells. However, malignant clones may have heterogenous cell-surface immunophenotypes including overlap with non-malignant cells. While leukemia-associated immunophenotypes and difference from normal approaches are used for flow cytometric assessment during and after treatment, such analysis may underrepresent true leukemia disease burden. Assessments of AML measurable residual disease (MRD) using flow cytometry and molecular methods have been reported as discrepant. Single-cell RNA sequencing experiments have recently attempted to distinguish malignant cells based on gene expression and/or immunophenotypic profiles alone. We hypothesized that single-cell genotyping of mutated transcript(s) coupled with broad surface proteome and transcriptome profiling could provide an integrated multimodal method for AML characterization. METHODS: We adapted the previously reported "genotyping of transcriptomes" (PMID: 31270458) to identify cells carrying the NPM1 type A mutation commonly seen, and typically stable throughout the disease course, in AML. Healthy human peripheral blood mononuclear cells (PBMC) were mixed with an AML cell line carrying NPM1 type A mutation (OCI-AML3) at 7:3 ratio and labelled with 163 oligo-tagged antibodies. Single cell 3'v3 gene expression- (GEX), antibody derived tag- (ADT) and genotyping of NPM1 (GNPM) -libraries (10X Genomics) were sequenced on the NovaSeq 6000 (Illumina). Results were processed using Seurat 4.0 toolkit. RESULTS: In total, 72% (n=1680) of barcoded cells could be genotyped for NPM1. Of the genotyped cells, 59% (n = 986) were not NPM1 mutated. Visualization using Uniform Manifold Approximation and Projection (UMAP) showed separation of healthy PBMCs and OCI-AML3 cells using protein data, confirmed by annotation using NPM1 genotyping (Figure 1). We found a significant positive correlation between mRNA and corresponding cell surface protein expressions in non-mutated (Pearson's coefficient, r = 0.502, p = 6.87e-11) and NPM1 mutated (r= 0.392, p= 7.5e-7) cells. Compared to non-mutated, NPM1 mutated cells showed nearly 14-fold higher NPM1 transcript levels. In addition, a total 63 proteins were highly expressed on the surface of NPM1 mutated cells (Figure 2). Among these, CD33 and CD36 showed maximum 8-fold increase in expression. Other highly expressed proteins with at least >2.5-fold change were cell adhesion molecules (including CD328, CD155, and CD56), extracellular matrix binding proteins (CD49a/b) and interleukin receptor (CD123). CONCLUSION: Overall, our results demonstrate proof of principle that high-throughput cell surface proteome, transcriptome and genotyping analysis can be simultaneously performed to comprehensively and confidently characterize individual AML cells. Patient-specific multiomics data with broad cell-surface proteomic screening may allow novel target identification for monitoring and/or therapeutic intervention. Ongoing work will now use this methodology to characterize a cohort of NPM1 mutated AML patient samples. Figure 1 Figure 1. Disclosures Hourigan: Sellas: Research Funding.

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Hand2 delineates mesothelium progenitors and is reactivated in mesothelioma

10.1101/2020.11.11.355693 ◽

2020 ◽

Author(s):

Karin D. Prummel ◽

Helena L. Crowell ◽

Susan Nieuwenhuize ◽

Eline C. Brombacher ◽

Stephan Daetwyler ◽

...

Keyword(s):

Gene Expression ◽

Single Cell ◽

Single Cell Analysis ◽

De Novo ◽

Primordial Germ Cells ◽

Gene Expression Signature ◽

Time Lapse ◽

Lineage Tracing ◽

Genetic Lineage ◽

Lateral Plate

AbstractThe mesothelium forms epithelial membranes that line the bodies cavities and surround the internal organs. Mesothelia widely contribute to organ homeostasis and regeneration, and their dysregulation can result in congenital anomalies of the viscera, ventral wall defects, and mesothelioma tumors. Nonetheless, the embryonic ontogeny and developmental regulation of mesothelium formation has remained uncharted. Here, we combine genetic lineage tracing, in toto live imaging, and single-cell transcriptomics in zebrafish to track mesothelial progenitor origins from the lateral plate mesoderm (LPM). Our single-cell analysis uncovers a post-gastrulation gene expression signature centered on hand2 that delineates distinct progenitor populations within the forming LPM. Combining gene expression analysis and imaging of transgenic reporter zebrafish embryos, we chart the origin of mesothelial progenitors to the lateral-most, hand2-expressing LPM and confirm evolutionary conservation in mouse. Our time-lapse imaging of transgenic hand2 reporter embryos captures zebrafish mesothelium formation, documenting the coordinated cell movements that form pericardium and visceral and parietal peritoneum. We establish that the primordial germ cells migrate associated with the forming mesothelium as ventral migration boundary. Functionally, hand2 mutants fail to close the ventral mesothelium due to perturbed migration of mesothelium progenitors. Analyzing mouse and human mesothelioma tumors hypothesized to emerge from transformed mesothelium, we find de novo expression of LPM-associated transcription factors, and in particular of Hand2, indicating the re-initiation of a developmental transcriptional program in mesothelioma. Taken together, our work outlines a genetic and developmental signature of mesothelial origins centered around Hand2, contributing to our understanding of mesothelial pathologies and mesothelioma.

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Single Cell Sequencing Reveals Glial Specific Responses to Tissue Processing & Enzymatic Dissociation in Mice and Humans

10.1101/2020.12.03.408542 ◽

2020 ◽

Author(s):

Samuel E. Marsh ◽

Tushar Kamath ◽

Alec J. Walker ◽

Lasse Dissing-Olesen ◽

Timothy R. Hammond ◽

...

Keyword(s):

Gene Expression ◽

Single Cell ◽

Human Tissue ◽

Ex Vivo ◽

Gene Expression Signature ◽

Enzymatic Digestion ◽

Careful Consideration ◽

Post Mortem ◽

Fresh Tissue ◽

Enzymatic Dissociation

AbstractA key aspect of nearly all single cell experiments is the necessity to dissociate intact tissues into single cell suspensions for processing. While many protocols have been optimized for optimal cell yield, they have often overlooked the effects that dissociation can have on ex vivo gene expression changes during this process. Microglia, the brain’s resident macrophages, are a highly dynamic population that are extremely sensitive to their microenvironment and have been shown to dramatically alter their transcriptome upon stimulation. We demonstrate that use of enzymatic dissociation methods on mouse central nervous system (CNS) tissue induces an aberrant gene expression signature in microglia that can significantly confound downstream analysis. To minimize this issue, we developed a flexible protocol, that can be used with existing enzymatic protocols for fresh tissue, to eliminate artifactual gene expression while allowing for increased cell type diversity and yield. We demonstrate efficacy of this protocol in analysis of diverse CNS cell types and sorted myeloid populations while using enzymatic dissociation. Generation of new and reanalysis of previously published human brain single nucleus RNAseq (snRNA-seq) datasets reveal that a similar signature is also present in post-mortem tissue. Through novel snRNA-seq analysis of acutely-resected neurosurgical tissue we demonstrate that this signature can be induced in human tissue due to technical differences in sample processing. These results provide key insight into the potential confounds of enzymatic digestion and provide a solution to allow for enzymatic digestion for scRNA-seq while avoiding ex vivo transcriptional artifacts. Analysis of human tissue reveals potential for artifacts in current and future snRNA-seq datasets that will require deeper analysis and careful consideration to separate true biology from artifacts related to post-mortem processes.

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Deciphering intratumoral heterogeneity using integrated clonal tracking and single-cell transcriptome analyses

10.21203/rs.3.rs-132486/v1 ◽

2021 ◽

Author(s):

Rong Lu ◽

HUMBERTO CONTRERAS-TRUJILLO ◽

JIYA EERDENG ◽

SAMIR AKRE ◽

DU JIANG ◽

...

Keyword(s):

Gene Expression ◽

Single Cell ◽

Dna Barcode ◽

Treatment Resistance ◽

Intratumoral Heterogeneity ◽

Integrated System ◽

Cellular Heterogeneity ◽

Gene Expression Signatures ◽

Cell Transcriptome ◽

Single Cell Transcriptome

Abstract Cellular heterogeneity is a major cause of treatment resistance in cancer. Despite recent advances in single-cell genomic and transcriptomic sequencing, it remains difficult to relate measured molecular profiles to the cellular activities underlying cancer. Here, we present an integrated experimental system that connects single cell gene expression to heterogeneous cancer cell growth, metastasis, and treatment response. Our system integrates single cell transcriptome profiling with DNA barcode based clonal tracking in patient-derived xenograft models. We show that leukemia cells exhibiting unique gene expression signatures respond to different chemotherapies in distinct but consistent manners across multiple mice. In addition, we uncover an unexpected yet common form of leukemia expansion that is spatially confined to the bone marrow of single anatomical sites and driven by cells with distinct gene expression signatures. Our integrated system directly and effectively interrogates the molecular and cellular basis of the intratumoral heterogeneity underlying disease progression and treatment resistance.

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Further investigation of the potential anti-neoplastic, anti-inflammatory and immunomodulatory actions of phenoxybenzamine using the Broad Institute CLUE platform

10.1101/767392 ◽

2019 ◽

Author(s):

Mario A. Inchiosa

Keyword(s):

Gene Expression ◽

Histone Deacetylase ◽

Histone Deacetylase Inhibitors ◽

Glycogen Synthase ◽

Human Tumor ◽

Gene Expression Signature ◽

Broad Institute ◽

Adrenergic Antagonist ◽

Gene Expression Signatures ◽

Anti Inflammatory

AbstractPrevious clinical studies with the FDA-approved alpha-adrenergic antagonist, phenoxybenzamine, showed apparent efficacy to reverse the symptoms and disabilities of the neuropathic condition, Complex Regional Pain Syndrome; also, the anatomic spread and intensity of this syndrome has a proliferative character and it was proposed that phenoxybenzamine may have an anti-inflammatory, immunomodulatory mode of action. A previous study gave evidence that phenoxybenzamine had anti-proliferative activity in suppression of growth in several human tumor cell cultures. The same report demonstrated that the drug possessed significant histone deacetylase inhibitory activity. Utilizing the Harvard/Massachusetts Institute of Technology Broad Institute genomic database, CLUE, the present study suggests that the gene expression signature of phenoxybenzamine in malignant cell lines is consistent with anti-inflammatory/immunomodulatory activity and suppression of tumor expansion by several possible mechanisms of action. Of particular note, phenoxybenzamine demonstrated signatures that were highly similar to those with glucocorticoid agonist activity. Also, gene expression signatures of phenoxbenzamine were consistent with several agents in each case that were known to suppress tumor proliferation, notably, protein kinase C inhibitors, Heat Shock Protein inhibitors, epidermal growth factor receptor inhibitors, and glycogen synthase kinase inhibitors. Searches in CLUE also confirmed the earlier observations of strong similarities between gene expression signatures of phenoxybenzamine and several histone deacetylase inhibitors.

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GENE-55. GENE EXPRESSION SIGNATURE ASSOCIATED WITH AGGRESSIVE GLIOBLASTOMA GROWTH IS ENRICHED IN CHROMATIN MODIFICATION AND STEMNESS TRANSCRIPTIONAL REGULATION PROGRAMS

Neuro-Oncology ◽

10.1093/neuonc/noz175.457 ◽

2019 ◽

Vol 21 (Supplement_6) ◽

pp. vi109-vi109

Author(s):

Artem Berezovsky ◽

Oluwademilade Nuga ◽

Yuling Meng ◽

Laila Poisson ◽

Houtan Noushmehr ◽

...

Keyword(s):

Gene Expression ◽

Transcriptional Regulation ◽

Median Survival ◽

Chromatin Modification ◽

Gene Expression Signature ◽

Illumina Hiseq ◽

Intrinsic Gene ◽

Gene Expression Signatures ◽

Novel Host ◽

Aggressive Tumor

Abstract Prognosis of patients diagnosed with IDHwt glioblastoma is influenced by known clinical and demographic factors, and likely by physiological characteristics. Our goal is to determine tumor-intrinsic gene expression signatures associated with aggressive tumor growth in patient-derived xenografts (PDXs). Cancer stem cell (CSC) lines established from 10 IDHwt glioblastoma tumors were implanted orthotopically in cohorts of 10 to 15 nude mice (3x10E5 viable cells/mouse), for development of PDX under uniform conditions. Mice were monitored and sacrificed when symptomatic. Five PDX lines, presenting median survival of 29 to 59 days were classified as short (S) survivors, and 5 lines with median survival between 111 and 134 days as long (L) survivors. RNA was isolated from terminal PDX tumors (n=3/line) and sequenced using Illumina HiSeq 2000. Differential gene expression analysis between tumors in S and L survival groups was conducted using the lmFit and eBayes, and genes were ranked by Benjamini-Hochberg adjusted P-values, set to adj.p< 0.05, resulting in 1663 genes upregulated and 1539 genes downregulated in the aggressive S group. Gene ontology analysis was performed using Metacore (Clarivate Analytics) and Metascape (http://metascape.org). Chromatin modification was significantly enriched in the aggressive tumor group (Metacore, p= 1x 10–12). Remarkably, 40% (654/1663) of the genes upregulated in the aggressive PDX tumors were co-expressed with an epigenetic master regulator in the TCGA glioblastoma RNAseq dataset (cBio Portal, q< 10E-15), and this subset was also highly enriched in chromatin modification, stemness transcriptional regulation and DNA repair (q=10E-45 to q=10E-13). These results indicate that novel host-independent prognostic gene expression signatures can be derived from the PDX models and underline the potential of epigenetic regulators as therapeutic target for aggressive glioblastomas. Our results further indicate that these models are suitable for testing a new generation of epigenetic drugs currently in pre-clinical and clinical development.

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De Novo Generation of Hit-like Molecules from Gene Expression Signatures Using Artificial Intelligence

10.26434/chemrxiv.7294388.v1 ◽

2018 ◽

Cited By ~ 4

Author(s):

Oscar Mendez-Lucio ◽

Benoit Baillif ◽

Djork-Arné Clevert ◽

David Rouquié ◽

Joerg Wichard

Keyword(s):

Gene Expression ◽

Artificial Intelligence ◽

De Novo ◽

Molecular Design ◽

Gene Expression Signature ◽

Cellular Level ◽

Generative Models ◽

Generative Adversarial Network ◽

Adversarial Network ◽

Gene Expression Signatures

Finding new molecules with a desired biological activity is an extremely difficult task. In this context, artificial intelligence and generative models have been used for molecular <i>de novo</i> design and compound optimization. Herein, we report the first generative model that bridges systems biology and molecular design conditioning a generative adversarial network with transcriptomic data. By doing this we could generate molecules that have high probability to produce a desired biological effect at cellular level. We show that this model is able to design active-like molecules for desired targets without any previous target annotation of the training compounds as long as the gene expression signature of the desired state is provided. The molecules generated by this model are more similar to active compounds than the ones identified by similarity of gene expression signatures, which is the state-of-the-art method for navigating compound-induced gene expression data. Overall, this method represents a novel way to bridge chemistry and biology to advance in the long and difficult road of drug discovery.

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