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  • 1
    In: Nature Communications, Springer Science and Business Media LLC, Vol. 14, No. 1 ( 2023-03-09)
    Abstract: Systemic immunity supports lifelong brain function. Obesity posits a chronic burden on systemic immunity. Independently, obesity was shown as a risk factor for Alzheimer’s disease (AD). Here we show that high-fat obesogenic diet accelerated recognition-memory impairment in an AD mouse model (5xFAD). In obese 5xFAD mice, hippocampal cells displayed only minor diet-related transcriptional changes, whereas the splenic immune landscape exhibited aging-like CD4 + T-cell deregulation. Following plasma metabolite profiling, we identified free N -acetylneuraminic acid (NANA), the predominant sialic acid, as the metabolite linking recognition-memory impairment to increased splenic immune-suppressive cells in mice. Single-nucleus RNA-sequencing revealed mouse visceral adipose macrophages as a potential source of NANA. In vitro, NANA reduced CD4 + T-cell proliferation, tested in both mouse and human. In vivo, NANA administration to standard diet-fed mice recapitulated high-fat diet effects on CD4 + T cells and accelerated recognition-memory impairment in 5xFAD mice. We suggest that obesity accelerates disease manifestation in a mouse model of AD via systemic immune exhaustion.
    Type of Medium: Online Resource
    ISSN: 2041-1723
    Language: English
    Publisher: Springer Science and Business Media LLC
    Publication Date: 2023
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  • 2
    In: Nature, Springer Science and Business Media LLC, Vol. 595, No. 7865 ( 2021-07-01), p. 107-113
    Type of Medium: Online Resource
    ISSN: 0028-0836 , 1476-4687
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    Language: English
    Publisher: Springer Science and Business Media LLC
    Publication Date: 2021
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  • 3
    In: Nature, Springer Science and Business Media LLC, Vol. 560, No. 7718 ( 2018-8), p. 319-324
    Type of Medium: Online Resource
    ISSN: 0028-0836 , 1476-4687
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    Language: English
    Publisher: Springer Science and Business Media LLC
    Publication Date: 2018
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  • 4
    In: Nature Medicine, Springer Science and Business Media LLC, Vol. 26, No. 8 ( 2020-08), p. 1271-1279
    Type of Medium: Online Resource
    ISSN: 1078-8956 , 1546-170X
    Language: English
    Publisher: Springer Science and Business Media LLC
    Publication Date: 2020
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  • 5
    In: Hepatology Communications, Ovid Technologies (Wolters Kluwer Health), Vol. 6, No. 4 ( 2022-04), p. 821-840
    Abstract: The critical functions of the human liver are coordinated through the interactions of hepatic parenchymal and non‐parenchymal cells. Recent advances in single‐cell transcriptional approaches have enabled an examination of the human liver with unprecedented resolution. However, dissociation‐related cell perturbation can limit the ability to fully capture the human liver’s parenchymal cell fraction, which limits the ability to comprehensively profile this organ. Here, we report the transcriptional landscape of 73,295 cells from the human liver using matched single‐cell RNA sequencing (scRNA‐seq) and single‐nucleus RNA sequencing (snRNA‐seq). The addition of snRNA‐seq enabled the characterization of interzonal hepatocytes at a single‐cell resolution, revealed the presence of rare subtypes of liver mesenchymal cells, and facilitated the detection of cholangiocyte progenitors that had only been observed during in vitro differentiation experiments. However, T and B lymphocytes and natural killer cells were only distinguishable using scRNA‐seq, highlighting the importance of applying both technologies to obtain a complete map of tissue‐resident cell types. We validated the distinct spatial distribution of the hepatocyte, cholangiocyte, and mesenchymal cell populations by an independent spatial transcriptomics data set and immunohistochemistry. Conclusion: Our study provides a systematic comparison of the transcriptomes captured by scRNA‐seq and snRNA‐seq and delivers a high‐resolution map of the parenchymal cell populations in the healthy human liver.
    Type of Medium: Online Resource
    ISSN: 2471-254X , 2471-254X
    Language: English
    Publisher: Ovid Technologies (Wolters Kluwer Health)
    Publication Date: 2022
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  • 6
    In: Cancer Research, American Association for Cancer Research (AACR), Vol. 82, No. 4_Supplement ( 2022-02-15), p. PD6-03-PD6-03
    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.
    Type of Medium: Online Resource
    ISSN: 0008-5472 , 1538-7445
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    Language: English
    Publisher: American Association for Cancer Research (AACR)
    Publication Date: 2022
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  • 7
    In: Cancer Research, American Association for Cancer Research (AACR), Vol. 80, No. 4_Supplement ( 2020-02-15), p. GS6-05-GS6-05
    Abstract: While recent studies have begun to elucidate the genomics of metastatic breast cancer (MBC), the transcriptional programs (progs) that drive the drug-resistant phenotype remain poorly understood. We prospectively collected biopsies from patients with MBC with detailed clinicopathologic features, including treatment and response characteristics. We profiled 29 biopsies by single-cell RNA seq, as well as bulk RNA-seq and whole exome sequencing on an additional 207 biopsies (analyzed snapshot, ongoing study). We analyzed these data to generate an atlas, delineating the cell-types, cell-states, and transcriptional progs. We profiled 100,470 single cell transcriptomes and generated a comprehensive MBC atlas of the tumor and tumor microenvironment (TME). We next inferred cell types and progs associated with clinicopathologic characteristics. For example, we found significant differences in the TME of liver metastases compared to other sites, consistent with immunosuppression in the hepatic space. In particular, liver metastases were depleted in activated B-cells (lower CD69, TCL1A, VPREB3, BCNP1, and several activation-related chemokines - CXCR4/5/6/7), and their infiltrating T-cells expressed lower levels of effector and cytotoxicity markers (table=T) including CD8, CD3, beta chemokines CCL4/CCL5, IFI16, PRF1, granzymes, and GNLY, and exceptionally low expression of antigen processing genes (T). To increase our power to make clinically relevant associations, we performed a joint analysis of the single-cell and bulk RNA-Seq data, to identify malignant cell progs related to specific oncogenic mutations, with implications for metastatic and drug-resistance phenotypes. For example, we characterized the oncogenic prog associated with activating estrogen receptor mutation (ESR1-mut). As expected, ESR1-mut prog overlaps with many known ER and luminal B markers (T). ESR1-mut prog also included specific Interferon-stimulated genes (ISGs) - IFI6, ISG15, IFIT1, STAT1, which are associated with tamoxifen resistance (T) and ECM-mediated regulation of apoptosis (T). These ISGs are predictive of poor prognosis among endocrine-treated patients (HR=1.69. p=1e-04, n=929, kmplotter). The ESR1-mut prog also included genes associated with cell-migration (SOX9, AGR2, TXNIP and several S100 genes). This suggests a role for ESR1 mutation in pathogenicity, beyond ligand-independent activation of ER signaling. A second malignant cell prog was associated with RB-null. While most of these genes were highly correlated with cell-cycle (R=0.4), some do not (HINT1, RHOC, SNRPB, ANAPC11, TMEM208, POLR2J). In spite of decoupling from proliferation, these still predict of poor outcome across breast cancer (HR=1.41, p= 6.0e-10, n=3951, kmplotter). We characterized the prog of high-grade tumors in our cohort, and found a strong association with RB-null prog (T). Similarly, we recovered additional mutation-specific oncogenic progs, including for TP53, GATA3, FOXA1, HER2, and FGFR mutants, forming a compendium of in-vivo oncogenic signatures. To the best of our knowledge these data represent the first integration of single cell and bulk RNA-seq data in MBC, resulting in a comprehensive single-cell-resolution transcriptional atlas, and a catalog of drug-resistance oncogenic progs with implications for immunotherapy and precision-oncology. Single-cell In-vivo programComparison Gene setAssociationP-value(top genes)(MSigDB name, PubMed ID, when applicable)Odds-ratio(Fisher''''s Exact test, two-sided)Depleted in liver T-cellsEffectors-cells markers4.077.29E-07(compared to other metastatic site - Bone, Skin, Chest-wall, Lymph node, Breast-met)(PMID: 28052254)""Antigen processing markers32.17.26E-07(PMID: 28052254)ESR1-mutVANTVEER_BREAST_CANCER_ESR1_UP5.82.30E-11(Enriched in ESR1-mutatated)(PMID: 11823860)“SMID_BREAST_CANCER_LUMINAL_B_UP5.898.10E-12“”DOANE_BREAST_CANCER_ESR1_UP6.30812.10E-09""BOWIE_RESPONSE_TO_TAMOXIFEN10.360.00036(PMID 17016442)""BECKER_TAMOXIFEN_RESISTANCE_UP7.81.20E-06(PMID 15657362)""BOWIE_RESPONSE_TO_EXTRACELLULAR_MATRIX8.240.0028(PMID 17016442)Enriched in RB-nullEnriched in High-grade (from our study)23.97.50E-05 Citation Format: Ofir Cohen, Daniel Abravanel, Michal Slyper, Johanna Klughammer, Judit Jane-Valbuena, Karla Helvie, Laura Dellostritto, Allison Frangieh, Sebastien Vigneau, Jingyi Wu, Angie Mayorga, Julia Waldman, Lan Nguyen, Danielle Dionne, Orr Ashenberg, Matan Hofree, Mike Cuoco, Christopher Rodman, Eric P Winer, Nancy Lin, Bruce Johnson, Asaf Rotem, Orit Rozenblatt-Rosen, Aviv Regev, Nikhil Wagle. A joint atlas of single-cell and bulk RNA-seq in metastatic breast cancer allows inference of oncogenic and drug-resistant transcriptional programs in malignant cells and the tumor microenvironment [abstract]. In: Proceedings of the 2019 San Antonio Breast Cancer Symposium; 2019 Dec 10-14; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2020;80(4 Suppl):Abstract nr GS6-05.
    Type of Medium: Online Resource
    ISSN: 0008-5472 , 1538-7445
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    Language: English
    Publisher: American Association for Cancer Research (AACR)
    Publication Date: 2020
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  • 8
    In: Science, American Association for the Advancement of Science (AAAS), Vol. 376, No. 6594 ( 2022-05-13)
    Abstract: Understanding and treating disease requires deep, systematic characterization of different cells and their interactions across human tissues and organs, along with characterization of the genetic variants that causally contribute to disease risk. Recent studies have combined single-cell atlases of specific human tissues and organs with genes associated with human disease to relate risk variants to likely cells of action. ​​However, it has been challenging to extend these studies to profile multiple tissues and organs across the body, conduct studies at population scale, and integrate cell atlases from multiple organs to yield unified insights. RATIONALE Because of the pleiotropy and specificity of disease-associated variants, systematically relating variants to cells and molecular processes requires analysis across multiple tissues and individuals. Prior cell atlases primarily relied on fresh tissue samples from a single organ or tissue. Single-nucleus RNA sequencing (snRNA-seq) can be applied to frozen, archived tissue and captures cell types that do not survive dissociation across many tissues. Deep learning methods can integrate data across individuals and tissues by controlling for batch effects while preserving biological variation. RESULTS We established a framework for multitissue human cell atlases and generated an atlas of 209,126 snRNA-seq profiles from eight tissue types across 16 individuals, archived as frozen tissue as part of the Genotype-Tissue Expression (GTEx) project. We benchmarked four protocols and show how to apply them in a pooled setting to enable larger studies. We integrated the cross-tissue atlas using a conditional variational autoencoder, annotated it with 43 broad and 74 fine categories, and demonstrated its use to decipher tissue residency, such as a macrophage dichotomy and lipid associations that are preserved across tissues, and tissue-specific fibroblast features, including lung alveolar fibroblasts with likely roles in mechanosensation. We relate cells to human disease biology and disease-risk genes for both rare and common diseases, including rare muscle disease gene groups enriched in distinct subsets of myonuclei and nonmyocytes, and cell type–specific enrichment of expression and splicing quantitative trait locus (QTL) target genes mapped to genome-wide association study loci. CONCLUSION Our framework will empower large, cross-tissue population and/or disease studies at single-cell resolution. These frameworks and the cross-tissue perspective provided here will form a basis for larger-scale future studies to improve our understanding of cross-tissue and cross-individual variation of cellular phenotypes in relation to disease-associated genetic variation. Cross-tissue snRNA-seq atlas in eight frozen, archived adult human tissues. Tissue sites and experimental pipeline (top row). The resulting atlas enables a cross-tissue census of tissue-specific and shared cell types (middle left). Differentiation trajectories and compositional analysis of dichotomous macrophage populations improve our understanding of tissue residency (middle center and right). Analyses of fibroblasts across tissues reveal tissue-specific and shared fibroblast features and their functional interpretation (bottom left). Robust and scalable computational methods enable comprehensive associations of monogenic and complex diseases to tissue-specific and shared cell populations (bottom center and right). E. mucosa, esophagus mucosa; E. muscularis, esophagus muscularis; Sk. muscle, skeletal muscle.
    Type of Medium: Online Resource
    ISSN: 0036-8075 , 1095-9203
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    Language: English
    Publisher: American Association for the Advancement of Science (AAAS)
    Publication Date: 2022
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  • 9
    In: Immunity, Elsevier BV, Vol. 52, No. 6 ( 2020-06), p. 1088-1104.e6
    Type of Medium: Online Resource
    ISSN: 1074-7613
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    Language: English
    Publisher: Elsevier BV
    Publication Date: 2020
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  • 10
    In: Nature Methods, Springer Science and Business Media LLC, Vol. 17, No. 8 ( 2020-08), p. 793-798
    Type of Medium: Online Resource
    ISSN: 1548-7091 , 1548-7105
    Language: English
    Publisher: Springer Science and Business Media LLC
    Publication Date: 2020
    detail.hit.zdb_id: 2163081-1
    SSG: 12
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