Differences in Cell Cycle Status Underlie Transcriptional Heterogeneity in the HSC Compartment

Lauridsen, Felicia Kathrine Bratt; Jensen, Tanja Lyholm; Rapin, Nicolas; Aslan, Derya; Wilhelmson, Anna S.; Pundhir, Sachin; Rehn, Matilda; Paul, Franziska; Giladi, Amir; Hasemann, Marie Sigurd; Serup, Palle; Amit, Ido; Porse, Bo T.

doi:10.1016/j.celrep.2018.06.057

Cited by 46 publications

(43 citation statements)

References 47 publications

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“…It was initially thought that specific transcriptional networks of individual HSCs were the major determinants for driving specific lineages ( Kee, 2011 ; Novershtern et al., 2011 ). However, similar to a recent study ( Lauridsen et al., 2018 ), we found that the transcriptional networks for myelopoiesis or lymphopoiesis were similar in HSCs/MPPs after BCG vaccination or Mtb infection. Rather, the magnitude of several key pathways, including IFN-I and heme/iron metabolism, differed considerably.…”

Section: Discussionsupporting

confidence: 90%

M. tuberculosis Reprograms Hematopoietic Stem Cells to Limit Myelopoiesis and Impair Trained Immunity

Khan

Downey

Sanz

et al. 2020

Cell

184

179

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Summary A greater understanding of hematopoietic stem cell (HSC) regulation is required for dissecting protective versus detrimental immunity to pathogens that cause chronic infections such as Mycobacterium tuberculosis ( Mtb ). We have shown that systemic administration of Bacille Calmette-Guérin (BCG) or β-glucan reprograms HSCs in the bone marrow (BM) via a type II interferon (IFN-II) or interleukin-1 (IL1) response, respectively, which confers protective trained immunity against Mtb . Here, we demonstrate that, unlike BCG or β-glucan, Mtb reprograms HSCs via an IFN-I response that suppresses myelopoiesis and impairs development of protective trained immunity to Mtb . Mechanistically, IFN-I signaling dysregulates iron metabolism, depolarizes mitochondrial membrane potential, and induces cell death specifically in myeloid progenitors. Additionally, activation of the IFN-I/iron axis in HSCs impairs trained immunity to Mtb infection. These results identify an unanticipated immune evasion strategy of Mtb in the BM that controls the magnitude and intrinsic anti-microbial capacity of innate immunity to infection.

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Section: Discussionsupporting

confidence: 90%

M. tuberculosis Reprograms Hematopoietic Stem Cells to Limit Myelopoiesis and Impair Trained Immunity

Khan

Downey

Sanz

et al. 2020

Cell

184

179

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“…Functional enrichment profiling of genes upregulated in L1-C iHPCs clusters 50 was consistent with previous scRNA-SEQ studies showing a large contribution of cell cycle genes to transcriptional heterogeneity observed in differentiating hematopoietic cells 51 , particularly stem and progenitor cells 52,53 . L1-C CD42a + iHPCs, clusters 1 and 3 were enriched in cell cycle-related biological processes, whereas cluster 7 showed broad enrichment in myeloid-and especially Mk/plateletassociated genes.…”

Section: Scrna Seq Identifies Novel Deregulated Pathways In Runx1 +/-supporting

confidence: 89%

RUNX1 haploinsufficiency causes a marked deficiency of megakaryocyte-biased hematopoietic progenitor cells: Mechanistic studies and drug correction

Estevez

Borst

Jarocha

et al. 2020

Preprint

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Patients with familial platelet disorder with a predisposition to myeloid malignancy (FPDMM) harbor germline monoallelic mutations in a key hematopoietic transcription factor RUNX1. Previous studies of FPDMM have focused on megakaryocyte (Mk) differentiation, and platelet production and signaling. However, the effects of RUNX1 haploinsufficiency on hematopoietic progenitor cells (HPCs) and subsequent megakaryopoiesis remains incomplete. To address this issue, we studied induced-pluripotent stem cell (iPSC)-derived HPCs (iHPCs) and Mks (iMks) from both patient-derived lines and a wildtype line modified to be RUNX1 haploinsufficient (RUNX1+/−), each compared to their isogenic wildtype control. All RUNX1+/− lines showed decreased iMk yield and depletion of a Mk-biased iHPC subpopulation. To investigate global and local gene expression changes underlying this iHPC shift, single-cell RNA sequencing was performed on sorted FPDMM and control iHPCs. We defined several cell subpopulations in FPDMM Mk-biased iHPCs. Analyses of gene sets upregulated in FPDMM iHPCs indicated enrichment for response to stress, regulation of signal transduction and response to cytokine gene sets. Immunoblotting studies in FPDMM iMks were consistent with these findings, but also identified augmented baseline c-Jun N-terminal kinase (JNK) phosphorylation, known to be activated by transforming growth factor β1 and cellular stressors. J-IN8 and RepSox, small drugs targeting these pathways, corrected quantitative defects in FPDMM iHPC production. These findings were confirmed in adult human CD34+-derived stem and progenitor cells transduced with lentiviral RUNX1 short-hairpin (sh) RNA to mimic RUNX1+/−. These mechanistic studies of the defect in megakaryopoiesis in FPDMM suggest druggable pathways for clinical management of thrombocytopenia in affected patients.Key pointsRUNX1 haploinsufficiency results in a deficiency of megakaryocyte-biased hematopoietic progenitor cells (HPCs).RUNX1 haploinsufficiency elevates druggable proinflammatory and TGFβR1-related pathways in HPCs.

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“…There are many reasons for differences in gene expression among cells, with arguably the most obvious candidates being differences in regulation among cell types, and differences in cell cycle phase among cells [11][12][13]. Cell type and cell cycle phase, while interesting to study directly, are often considered confounders in single cell studies that focus on other factors influencing gene expression [14][15][16], such as genotype, treatment [17], or developmental time [5,18]. The ability to characterize, correctly classify, and correct for cell type and cell cycle phase are therefore important, even in studies that do not specifically aim to study either of these factors.…”

Section: Introductionmentioning

confidence: 99%

Characterizing and inferring quantitative cell cycle phase in single-cell RNA-seq data analysis

Hsiao¹,

Tung

Blischak³

et al. 2019

Preprint

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AbstractCellular heterogeneity in gene expression is driven by cellular processes such as cell cycle and cell-type identity, and cellular environment such as spatial location. The cell cycle, in particular, is thought to be a key driver of cell-to-cell heterogeneity in gene expression, even in otherwise homogeneous cell populations. Recent advances in single-cell RNA-sequencing (scRNA-seq) facilitate detailed characterization of gene expression heterogeneity, and can thus shed new light on the processes driving heterogeneity. Here, we combined fluorescence imaging with scRNA-seq to measure cell cycle phase and gene expression levels in human induced pluripotent stem cells (iPSCs). Using these data, we developed a novel approach to characterize cell cycle progression. While standard methods assign cells to discrete cell cycle stages, our method goes beyond this, and quantifies cell cycle progression on a continuum. We found that, on average, scRNA-seq data from only five genes predicted a cell’s position on the cell cycle continuum to within 14% of the entire cycle, and that using more genes did not improve this accuracy. Our data and predictor of cell cycle phase can directly help future studies to account for cell-cycle-related heterogeneity in iPSCs. Our results and methods also provide a foundation for future work to characterize the effects of the cell cycle on expression heterogeneity in other cell types.

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Differences in Cell Cycle Status Underlie Transcriptional Heterogeneity in the HSC Compartment

Cited by 46 publications

References 47 publications

M. tuberculosis Reprograms Hematopoietic Stem Cells to Limit Myelopoiesis and Impair Trained Immunity

M. tuberculosis Reprograms Hematopoietic Stem Cells to Limit Myelopoiesis and Impair Trained Immunity

RUNX1 haploinsufficiency causes a marked deficiency of megakaryocyte-biased hematopoietic progenitor cells: Mechanistic studies and drug correction

Characterizing and inferring quantitative cell cycle phase in single-cell RNA-seq data analysis

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