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Batorsky R, Ceasrine AM, Shook LL, Kislal S, Bordt EA, Devlin BA, Perlis RH, Slonim DK, Bilbo SD, Edlow AG. Hofbauer cells and fetal brain microglia share transcriptional profiles and responses to maternal diet-induced obesity. Cell Rep 2024; 43:114326. [PMID: 38848212 DOI: 10.1016/j.celrep.2024.114326] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/13/2023] [Revised: 04/25/2024] [Accepted: 05/22/2024] [Indexed: 06/09/2024] Open
Abstract
Maternal immune activation is associated with adverse offspring neurodevelopmental outcomes, many mediated by in utero microglial programming. As microglia remain inaccessible throughout development, identification of noninvasive biomarkers reflecting fetal brain microglial programming could permit screening and intervention. We used lineage tracing to demonstrate the shared ontogeny between fetal brain macrophages (microglia) and fetal placental macrophages (Hofbauer cells) in a mouse model of maternal diet-induced obesity, and single-cell RNA-seq to demonstrate shared transcriptional programs. Comparison with human datasets demonstrated conservation of placental resident macrophage signatures between mice and humans. Single-cell RNA-seq identified common alterations in fetal microglial and Hofbauer cell gene expression induced by maternal obesity, as well as sex differences in these alterations. We propose that Hofbauer cells, which are easily accessible at birth, provide insights into fetal brain microglial programs and may facilitate the early identification of offspring vulnerable to neurodevelopmental disorders.
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Affiliation(s)
- Rebecca Batorsky
- Data Intensive Studies Center, Tufts University, Medford, MA, USA
| | - Alexis M Ceasrine
- Department of Psychology and Neuroscience, Duke University, Durham, NC, USA
| | - Lydia L Shook
- Division of Maternal-Fetal Medicine, Department of Ob/Gyn, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA; Vincent Center for Reproductive Biology, Massachusetts General Hospital Research Institute, Massachusetts General Hospital, Boston, MA, USA
| | - Sezen Kislal
- Vincent Center for Reproductive Biology, Massachusetts General Hospital Research Institute, Massachusetts General Hospital, Boston, MA, USA
| | - Evan A Bordt
- Department of Pediatrics, Lurie Center for Autism, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Benjamin A Devlin
- Department of Psychology and Neuroscience, Duke University, Durham, NC, USA
| | - Roy H Perlis
- Department of Psychiatry and Center for Genomic Medicine, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Donna K Slonim
- Department of Computer Science, Tufts University, Medford, MA, USA
| | - Staci D Bilbo
- Department of Psychology and Neuroscience, Duke University, Durham, NC, USA; Department of Neurobiology, Duke University, Durham, NC, USA; Lurie Center for Autism, Massachusetts General Hospital, Boston, MA, USA
| | - Andrea G Edlow
- Division of Maternal-Fetal Medicine, Department of Ob/Gyn, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA; Vincent Center for Reproductive Biology, Massachusetts General Hospital Research Institute, Massachusetts General Hospital, Boston, MA, USA.
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2
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Gardner AL, Jost TA, Brock A. Computational identification of surface markers for isolating distinct subpopulations from heterogeneous cancer cell populations. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.05.28.596337. [PMID: 38854060 PMCID: PMC11160629 DOI: 10.1101/2024.05.28.596337] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/11/2024]
Abstract
Intratumor heterogeneity reduces treatment efficacy and complicates our understanding of tumor progression. There is a pressing need to understand the functions of heterogeneous tumor cell subpopulations within a tumor, yet biological systems to study these processes in vitro are limited. With the advent of single-cell RNA sequencing (scRNA-seq), it has become clear that some cancer cell line models include distinct subpopulations. Heterogeneous cell lines offer a unique opportunity to study the dynamics and evolution of genetically similar cancer cell subpopulations in controlled experimental settings. Here, we present clusterCleaver, a computational package that uses metrics of statistical distance to identify candidate surface markers maximally unique to transcriptomic subpopulations in scRNA-seq which may be used for FACS isolation. clusterCleaver was experimentally validated using the MDA-MB-231 and MDA-MB-436 breast cancer cell lines. ESAM and BST2/tetherin were experimentally confirmed as surface markers which identify and separate major transcriptomic subpopulations within MDA-MB-231 and MDA-MB-436 cells, respectively. clusterCleaver is a computationally efficient and experimentally validated workflow for identification and enrichment of distinct subpopulations within cell lines which paves the way for studies on the coexistence of cancer cell subpopulations in well-defined in vitro systems.
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Affiliation(s)
- Andrea L. Gardner
- Department of Biomedical Engineering, The University of Texas at Austin
| | - Tyler A. Jost
- Department of Biomedical Engineering, The University of Texas at Austin
| | - Amy Brock
- Department of Biomedical Engineering, The University of Texas at Austin
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3
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Batorsky R, Ceasrine AM, Shook LL, Kislal S, Bordt EA, Devlin BA, Perlis RH, Slonim DK, Bilbo SD, Edlow AG. Hofbauer cells and fetal brain microglia share transcriptional profiles and responses to maternal diet-induced obesity. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.12.16.571680. [PMID: 38187648 PMCID: PMC10769274 DOI: 10.1101/2023.12.16.571680] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/09/2024]
Abstract
Maternal immune activation is associated with adverse offspring neurodevelopmental outcomes, many mediated by in utero microglial programming. As microglia remain inaccessible throughout development, identification of noninvasive biomarkers reflecting fetal brain microglial programming could permit screening and intervention. We used lineage tracing to demonstrate the shared ontogeny between fetal brain macrophages (microglia) and fetal placental macrophages (Hofbauer cells) in a mouse model of maternal diet-induced obesity, and single-cell RNA-seq to demonstrate shared transcriptional programs. Comparison with human datasets demonstrated conservation of placental resident macrophage signatures between mice and humans. Single-cell RNA-seq identified common alterations in fetal microglial and Hofbauer cell gene expression induced by maternal obesity, as well as sex differences in these alterations. We propose that Hofbauer cells, which are easily accessible at birth, provide novel insights into fetal brain microglial programs, and may facilitate the early identification of offspring vulnerable to neurodevelopmental disorders in the setting of maternal exposures.
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Affiliation(s)
| | - Alexis M. Ceasrine
- Department of Psychology and Neuroscience, Duke University, Durham, NC, USA
| | - Lydia L. Shook
- Division of Maternal-Fetal Medicine, Department of Ob/Gyn, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts, USA
- Vincent Center for Reproductive Biology, Massachusetts General Hospital Research Institute, Massachusetts General Hospital, Boston, Massachusetts, USA
| | - Sezen Kislal
- Vincent Center for Reproductive Biology, Massachusetts General Hospital Research Institute, Massachusetts General Hospital, Boston, Massachusetts, USA
| | - Evan A. Bordt
- Department of Pediatrics, Lurie Center for Autism, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts, USA
| | - Benjamin A. Devlin
- Department of Psychology and Neuroscience, Duke University, Durham, NC, USA
| | - Roy H. Perlis
- Department of Psychiatry and Center for Genomic Medicine, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts, USA
| | - Donna K. Slonim
- Department of Computer Science, Tufts University, Medford, MA
| | - Staci D. Bilbo
- Department of Psychology and Neuroscience, Duke University, Durham, NC, USA
- Department of Neurobiology, Duke University, Durham, NC, USA
- Lurie Center for Autism, Massachusetts General Hospital, Boston, MA
| | - Andrea G. Edlow
- Division of Maternal-Fetal Medicine, Department of Ob/Gyn, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts, USA
- Vincent Center for Reproductive Biology, Massachusetts General Hospital Research Institute, Massachusetts General Hospital, Boston, Massachusetts, USA
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4
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Maden SK, Kwon SH, Huuki-Myers LA, Collado-Torres L, Hicks SC, Maynard KR. Challenges and opportunities to computationally deconvolve heterogeneous tissue with varying cell sizes using single-cell RNA-sequencing datasets. Genome Biol 2023; 24:288. [PMID: 38098055 PMCID: PMC10722720 DOI: 10.1186/s13059-023-03123-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/11/2023] [Accepted: 11/24/2023] [Indexed: 12/17/2023] Open
Abstract
Deconvolution of cell mixtures in "bulk" transcriptomic samples from homogenate human tissue is important for understanding disease pathologies. However, several experimental and computational challenges impede transcriptomics-based deconvolution approaches using single-cell/nucleus RNA-seq reference atlases. Cells from the brain and blood have substantially different sizes, total mRNA, and transcriptional activities, and existing approaches may quantify total mRNA instead of cell type proportions. Further, standards are lacking for the use of cell reference atlases and integrative analyses of single-cell and spatial transcriptomics data. We discuss how to approach these key challenges with orthogonal "gold standard" datasets for evaluating deconvolution methods.
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Affiliation(s)
- Sean K Maden
- Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
| | - Sang Ho Kwon
- Lieber Institute for Brain Development, Johns Hopkins Medical Campus, Baltimore, MD, USA
- The Solomon H. Snyder Department of Neuroscience, Johns Hopkins School of Medicine, Baltimore, MD, USA
| | - Louise A Huuki-Myers
- Lieber Institute for Brain Development, Johns Hopkins Medical Campus, Baltimore, MD, USA
| | - Leonardo Collado-Torres
- Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
- Lieber Institute for Brain Development, Johns Hopkins Medical Campus, Baltimore, MD, USA
| | - Stephanie C Hicks
- Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA.
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, USA.
- Center for Computational Biology, Johns Hopkins University, Baltimore, MD, USA.
- Malone Center for Engineering in Healthcare, Johns Hopkins University, Baltimore, MD, USA.
| | - Kristen R Maynard
- Lieber Institute for Brain Development, Johns Hopkins Medical Campus, Baltimore, MD, USA.
- The Solomon H. Snyder Department of Neuroscience, Johns Hopkins School of Medicine, Baltimore, MD, USA.
- Department of Psychiatry and Behavioral Sciences, Johns Hopkins School of Medicine, Baltimore, MD, USA.
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5
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Tangherloni A, Riva SG, Myers B, Buffa FM, Cazzaniga P. MAGNETO: Cell type marker panel generator from single-cell transcriptomic data. J Biomed Inform 2023; 147:104510. [PMID: 37797704 DOI: 10.1016/j.jbi.2023.104510] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2023] [Revised: 09/12/2023] [Accepted: 09/29/2023] [Indexed: 10/07/2023]
Abstract
Single-cell RNA sequencing experiments produce data useful to identify different cell types, including uncharacterized and rare ones. This enables us to study the specific functional roles of these cells in different microenvironments and contexts. After identifying a (novel) cell type of interest, it is essential to build succinct marker panels, composed of a few genes referring to cell surface proteins and clusters of differentiation molecules, able to discriminate the desired cells from the other cell populations. In this work, we propose a fully-automatic framework called MAGNETO, which can help construct optimal marker panels starting from a single-cell gene expression matrix and a cell type identity for each cell. MAGNETO builds effective marker panels solving a tailored bi-objective optimization problem, where the first objective regards the identification of the genes able to isolate a specific cell type, while the second conflicting objective concerns the minimization of the total number of genes included in the panel. Our results on three public datasets show that MAGNETO can identify marker panels that identify the cell populations of interest better than state-of-the-art approaches. Finally, by fine-tuning MAGNETO, our results demonstrate that it is possible to obtain marker panels with different specificity levels.
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Affiliation(s)
- Andrea Tangherloni
- Department of Computing Sciences, Bocconi University, Via Guglielmo Röntgen 1, Milan, 20136, Italy; Bocconi Institute for Data Science and Analytics, Bocconi University, Via Guglielmo Röntgen 1, Milan, 20136, Italy; Department of Human and Social Sciences, University of Bergamo, Piazzale S. Agostino 2, Bergamo, 24129, Italy.
| | - Simone G Riva
- Weatherall Institute of Molecular Medicine, Radcliffe Department of Medicine, University of Oxford, Headley Way, Oxford, OX3 9DS, United Kingdom
| | - Brynelle Myers
- Wellcome Centre for Human Genetics, University of Oxford, Roosevelt Drive, Oxford, OX3 7BN, United Kingdom
| | - Francesca M Buffa
- Department of Computing Sciences, Bocconi University, Via Guglielmo Röntgen 1, Milan, 20136, Italy; Bocconi Institute for Data Science and Analytics, Bocconi University, Via Guglielmo Röntgen 1, Milan, 20136, Italy; Department of Oncology, University of Oxford, Old Road Campus Research Building, Oxford, OX3 7DQ, United Kingdom
| | - Paolo Cazzaniga
- Department of Human and Social Sciences, University of Bergamo, Piazzale S. Agostino 2, Bergamo, 24129, Italy; Bicocca Bioinformatics, Biostatistics, and Bioimaging Centre - B4, Via Follereau 3, Vedano al Lambro, 20854, Italy
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Lewis JE, Hergott CB. The Immunophenotypic Profile of Healthy Human Bone Marrow. Clin Lab Med 2023; 43:323-332. [PMID: 37481314 DOI: 10.1016/j.cll.2023.04.003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/24/2023]
Abstract
Flow cytometry enables multiparametric characterization of hematopoietic cell immunophenotype. Deviations from normal immunophenotypic patterns comprise a cardinal feature of many hematopoietic neoplasms, underscoring the ongoing essentiality of flow cytometry as a diagnostic tool. However, understanding of aberrant hematopoiesis requires an equal understanding of normal hematopoiesis as a comparator. In this review, we outline key features of healthy adult hematopoiesis and lineage specification as illuminated by flow cytometry and provide diagrams illustrating what a diagnostician may observe in flow cytometric plots. These features provide a profile of baseline hematopoiesis, to which clinical samples with suspected neoplasia may be compared.
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Affiliation(s)
- Joshua E Lewis
- Department of Pathology, Brigham and Women's Hospital, 75 Francis Street, Boston, MA 02115, USA; Harvard Medical School, 25 Shattuck St, Boston, MA 02115, USA
| | - Christopher B Hergott
- Department of Pathology, Brigham and Women's Hospital, 75 Francis Street, Boston, MA 02115, USA; Harvard Medical School, 25 Shattuck St, Boston, MA 02115, USA.
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