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Single-cell analysis of mixed-lineage states leading to a binary cell fate choice
Open accessAbstract
Delineating hierarchical cellular states, including rare intermediates and the networks of regulatory genes that orchestrate cell-type specification, are continuing challenges for developmental biology. Single-cell RNA sequencing is greatly accelerating such research, given its power to provide comprehensive descriptions of genomic states and their presumptive regulators. Haematopoietic multipotential progenitor cells, as well as bipotential intermediates, manifest mixed-lineage patterns of gene expression at a single-cell level. Such mixed-lineage states may reflect the molecular priming of different developmental potentials by co-expressed alternative-lineage determinants, namely transcription factors. Although a bistable gene regulatory network has been proposed to regulate the specification of either neutrophils or macrophages, the nature of the transition states manifested in vivo, and the underlying dynamics of the cell-fate determinants, have remained elusive. Here we use single-cell RNA sequencing coupled with a new analytic tool, iterative clustering and guide-gene selection, and clonogenic assays to delineate hierarchical genomic and regulatory states that culminate in neutrophil or macrophage specification in mice. We show that this analysis captured prevalent mixed-lineage intermediates that manifested concurrent expression of haematopoietic stem cell/progenitor and myeloid progenitor cell genes. It also revealed rare metastable intermediates that had collapsed the haematopoietic stem cell/progenitor gene expression programme, instead expressing low levels of the myeloid determinants, Irf8 and Gfi1 (refs 9, 10, 11, 12, 13). Genetic perturbations and chromatin immunoprecipitation followed by sequencing revealed Irf8 and Gfi1 as key components of counteracting myeloid-gene-regulatory networks. Combined loss of these two determinants ‘trapped’ the metastable intermediate. We propose that mixed-lineage states are obligatory during cell-fate specification, manifest differing frequencies because of their dynamic instability and are dictated by counteracting gene-regulatory networks.
Overview
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TL;DR
Single-cell RNA-Seq and ICGS resolved nine hierarchical myeloid states and identified prevalent and rare mixed-lineage intermediates during neutrophil-versus-macrophage specification. Irf8 and Gfi1 act as counteracting determinants of a metastable transition state that can be “trapped” by combined loss of both factors.
Key findings
- ICGS resolved nine hierarchically ordered cellular states—HSCP-1, HSCP-2, Meg, Eryth, Multi-Lin*, MDP, Mono, Gran, and Myelocyte—whose inferred developmental order agreed with the experimentally determined hematopoietic sequence.
- A prevalent Multi-Lin* state co-expressed HSCP and myeloid progenitor genes (Ctsg, Mpo, Elane) and the TFs Gata2, Meis1, PU.1, and C/EBPα, with variable priming of megakaryocytic, erythroid, granulocytic, and monocytic genes.
- Rare metastable intermediates expressed low levels of both Irf8 and Gfi1; Irf8hi GMPs were specified monocytic progenitors (CFU-M), Irf8− GMPs contained specified granulocytes (CFU-G) and bipotential progenitors (CFU-GM), and IG2s gave equal proportions of monocytic and granulocytic colonies.
- ChIP-Seq showed Irf8 peaks enriched for EICE motifs co-bound by PU.1, shared Gfi1/Irf8 regions accessible in GMPs, and increased H3K4me2 at shared regions upon Gfi1 loss or Lsd1 inhibition, correlating with enhanced monocytic potential.
Methods
- scRNA-Seq was performed on mouse LSK, CMP, GMP, and LKCD34+ cells using the C1 Single-Cell Auto Prep System; 96 LSK, 96 CMP, 136 GMP, and 66 LKCD34+ libraries were sequenced, with QC failures in 3 LSK, 2 CMP, 4 GMP, and 3 LKCD34+ samples. Additional scRNA-Seq was performed on GG1s, IG2s, and Irf8−/−Gfi1−/− GMP-like cells.
- ICGS used pairwise correlation of dynamically expressed genes and iterative clustering with pattern-specific guide genes, excluding cell-cycle genes; results were compared with six independent computational approaches and annotated using GO-Elite/ImmGen enrichment.
- ChIP-Seq for Gfi1, Irf8, and H3K4me2 was performed in GMPs, with external Cebpα ChIP-Seq and ATAC-Seq data; functional assays included CFU assays, flow cytometry, RT-PCR, and an inducible Gfi1 allele.
Limitations
- The paper does not state a formal limitations section.
- Evident from the text: the study is based on mouse bone marrow populations, and human relevance is not tested.
- Evident from the text: rare intermediates are inferred partly from population frequencies, and the Gfi1-GFP reporter expresses stable GFP, which the authors note can over-estimate Gfi1 expression and likely accounts for higher GFP in GG3s despite very low Gfi1 transcripts.
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A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Similarly, clustering of LKCD34+ cells recreated the entire developmental ordering with granulocytic precursors at one end of the continuum (Extended Data Fig. 6b).”
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In the paper
“Genetic perturbations and ChIP-Seq revealed Irf8 and Gfi1 as key components of counteracting myeloid-gene-regulatory networks.”
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In the paper
“Thus, during steady-state myelopoiesis a prevalent mixed-lineage state is encountered that expresses HSCP and myeloid progenitor genes (Ctsg, Mpo, and Elane), while displaying molecular priming of erythrocytic, megakaryocytic, granulocytic and monocytic potentials.”
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“Strikingly, similar binding patterns for Gfi1 and Irf8 were seen on the Irf8, Klf4 and Zeb2 genes (Extended Data Fig. 8a).”
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In the paper
“Notably, Gfi1 and Irf8, which are required for normal granulopoiesis and monopoiesis, respectively9,11,12,24,25, displayed strong partitioning within granulocyte- versus monocyte-specified cells (Fig. 1f).”
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIn the paper
“Gene expression patterns of Csf1r, Flt3 and Cx3cr1 suggested that both CMP and GMP contain macrophage/dendritic cell precursors (MDP: CX3CR1+CD115+CD135+)18, which was confirmed by flow cytometry (Extended Data Fig. 6d–f).”
What the sources say
- Not checked yetcites [18] Auffray, C. et al. CX3CR1+ CD115+ CD135+ common macrophage/DC precursors and the role of CX3CR1 in their response to inflammation. J. Exp. Med. 206, 595–606 (2009) source
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In the paper
“Clustering of Pearson-correlation coefficients for ICGS-delineated TF-gene pairs (Fig. 1d, e, Extended Data Fig. 6g–j), revealed three distinct regulatory states within GMPs (Fig. 1e).”
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passes- Rests on an assumption: Each ICGS-delineated cellular state has an underlying regulatory state characterized by distinct combinations of transcription factors.
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In the paper
“Analysis of GMPs using an Irf8-GFP reporter (Fig. 3a) and CD115 (Csf1r) revealed two major Irf8-GMP subpopulations (IG1 and IG3) and a minor intermediate (IG2) (Fig. 3b).”
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In the paper
“Conversely Gfi1 transcripts were anti-correlated with Irf8 and Csf1r (Fig. 3c).”
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In the paper
“Importantly, loss of either TF reduced the heterogeneity of genomic states manifested at the single-cell level (Fig. 2a).”
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In the paper
“In Csf1r+ GMP, inducing Gfi1 repressed monocytic genes (including Irf8), and induced neutrophil genes in a dose-dependent manner (Fig. 2d).”
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In the paper
“This confirmed an established regulatory relationship (e.g. Irf8-Klf423), and suggested new regulatory interactions (Irf8-Zeb2 and Gfi1-Per3).”
What the sources say
- Not checked yetcites [23] Kurotaki, D. et al. Essential role of the IRF8-KLF4 transcription factor cascade in murine monocyte differentiation. Blood 121, 1839–1849 (2013) source
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In the paper
“GG2 cells expressed highest levels of Gfi1 and represented specified granulocytic progenitors, while GG3 cells, which expressed highest levels of Irf8 were oppositely specified as monocytic progenitors (Fig. 3f–h).”
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“Genes located near the shared genomic regions were associated with monocytic-dendritic-precursors (ImmGen) or abnormal mononuclear cell morphology (Mouse Phenotype Ontology), and were reciprocally dysregulated in Irf8−/− or Gfi1−/− GMPs (Extended Data Fig. 7f).”
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In the paper
“Intriguingly, the IG2s, which expressed low levels of Irf8 and Gfi1 (Fig. 3b, c), appeared to represent cells poised to undergo specification as they gave rise to equal proportion of monocytic and granulocytic colonies.”
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“In agreement with regulatory state (Fig. 1e) and loss-of-function analyses (Fig. 2a), key TFs were reciprocally altered by increased expression of Gfi1; Klf4, Zeb2 and Irf5 were repressed whereas Ets1 and Per3 were induced.”
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In the paper
“Notably, Irf8 peaks were enriched for EICE motifs, which are co-bound by PU.126.”
What the sources say
- Not checked yetcites [26] Brass, A. L., Zhu, A. Q. & Singh, H. Assembly requirements of PU.1-Pip (IRF-4) activator complexes: inhibiting function in vivo using fused dimers. EMBO J. 18, 977–991 (1999) source
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In the paper
“Gfi1 induction in GMP increased granulocytic and diminished monocytic potential (Extended Data Fig. 8c, d).”
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In the paper
“Colony forming unit (CFU) assays demonstrated that Irf8hi GMPs (IG3) were specified monocytic progenitors (CFU-M); whereas Irf8− GMPs (that expressed highest levels of Gfi1) comprised of specified granulocytes (CFU-G) as well as bipotential progenitors (CFU-GM) (Fig. 3d).”
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In the paper
“The remaining two groups of cells expressed low levels of Gfi1 and Irf8 along with the myeloid genes Etv6, Mpo, Elane, Hax1, a subset of these expressed higher levels of Irf8 along with Cybb and Ly6a (Fig. 4a).”
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In the paper
“HOPACH generated clusters using ICGS for mouse intestinal organoids1 for the discovery of rare cell populations.”
§ Single-cell analysis of mixed-lineage states leading to a binary cell fate choice > Extended Data
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“Seurat hierarchical relationships between the predicted cell states, based on 161 differentially expressed genes.”
§ Single-cell analysis of mixed-lineage states leading to a binary cell fate choice > Extended Data
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“Significant changes in expression of key genes between Irf8−/− versus Irf8-high WT GMP, or Gfi1−/− versus Gfi1-high WT GMP are noted (* p<0.05, ** p<0.01, *** p<0.001 Benjamini-Hochberg adjusted).”
§ Single-cell analysis of mixed-lineage states leading to a binary cell fate choice > Extended Data
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“Seurat t-SNE displayed output for the 9 predicted cell states.”
§ Single-cell analysis of mixed-lineage states leading to a binary cell fate choice > Extended Data
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“We note induction of Irf8 at low levels is associated with loss of the multipotential program but is not accompanied by specification of monocytes.”
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“HSCP gene expression waned in IG2s and they co-expressed Gfi1 and Irf8.”
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“HOPACH generated clusters using ICGS for human myoblast differentiation, without inclusion of cell cycle associated genes.”
§ Single-cell analysis of mixed-lineage states leading to a binary cell fate choice > Extended Data
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In the paper
“Of the 96 LSK, 96 CMP, 136 GMP and 66 LKCD34+ libraries that were sequenced, 3 LSK, 2 CMP, 4 GMP and 3 LKCD34+ samples failed the QC analysis.”
§ Single-cell analysis of mixed-lineage states leading to a binary cell fate choice > Extended Data
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In the paper
“GG2 cells expressed highest levels of Gfi1 and represented specified granulocytic progenitors, while GG3 cells, which expressed highest levels of Irf8 were oppositely specified as monocytic progenitors (Fig. 3f–h).”
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“To test this, we analyzed IG2s (Fig. 4a, Extended Data Fig. 10i), which lack bipotential progenitors (CFU-GM) and are highly enriched for cells undergoing lineage specification, resulting in CFU-G and CFU-M.”
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“Note that Irf8−/− and Gfi1−/− GMP continue to express non-productive transcripts emanating from the mutant Gfi1 and Irf8 alleles.”
§ Single-cell analysis of mixed-lineage states leading to a binary cell fate choice > Extended Data
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“The second cluster down regulated most HSCP genes except Gata1 and expressed Gfi1, Il5ra, Prg2 and Epx.”
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“Heatmap of the t-SNE based cell-state ordering and the top 150 most significant RaceID genes associated with each of the 14 k-means clusters.”
§ Single-cell analysis of mixed-lineage states leading to a binary cell fate choice > Extended Data
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“Similarly, an intermediate level of Gfi1 expression is associated with loss of the multipotential program, but a further increase in its expression coincides with neutrophil specification.”
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“These cells were not contaminants as they expressed CD16/32 and CD34 (Extended Data Fig. 9e, f), and corresponded to the bipotential cells (CFU-GM) within the GG1 population (see below).”
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“Cell cycle associated genes were enriched (z-score>1.96) in Gfi1−/− and depleted (z-score <−1.96) in the Irf8−/− GMPs.”
§ Single-cell analysis of mixed-lineage states leading to a binary cell fate choice > Extended Data
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“HOPACH generated clusters derived using ICGS for human scRNA-Seq data for pre-implantation embryos and embryonic stem cells.”
§ Single-cell analysis of mixed-lineage states leading to a binary cell fate choice > Extended Data
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“Single-cell RNA-Seq library fragment distribution before and after optimization reveals an increase in reads that can be mapped to the genome (reads mapping to a single genomic/transcriptomic location using BowTie2/TopHat2; 61.8% before and 83.5% after optimization).”
§ Single-cell analysis of mixed-lineage states leading to a binary cell fate choice > Extended Data
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“Lsd1 inhibition results monocytic colony formation and increased Irf8 expression.”
§ Single-cell analysis of mixed-lineage states leading to a binary cell fate choice > Extended Data
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In the paper
“We propose that mixed-lineage states are obligatory during cell-fate specification and manifest differing frequencies because of their “dynamic instability”, dictated by counteracting gene-regulatory networks.”
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
- Rests on an assumption: Mixed-lineage states reflect molecular priming of developmental potentials by co-expressed alternate-lineage determinants, namely transcription factors.
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In the paper
“mouse manipulations were reviewed and approved by the Children's Hospital Research Foundation Institutional Animal Care and Use Committee (Protocol Number IACUC2013-0090).”
§ Single-cell analysis of mixed-lineage states leading to a binary cell fate choice > Methods > Mice
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“Accordingly, we propose that IG2s manifest “dynamic instability” because of the counter acting functions of Irf8 and Gfi1 and that this metastable state is “trapped” by the elimination of both developmental determinants.”
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- Rests on an assumption: Mixed-lineage states reflect molecular priming of developmental potentials by co-expressed alternate-lineage determinants, namely transcription factors.
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In the paper
“Differentially expressed genes were identified using AltAnalyze using a FDR adjusted empirical Bayes moderated t-test p<0.05.”
§ Single-cell analysis of mixed-lineage states leading to a binary cell fate choice > Methods > RNA-Seq and ChIP-Seq Data Processing
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“For flow cytometric statistics, a t-test was performed from at least 3 independent experiments.”
§ Single-cell analysis of mixed-lineage states leading to a binary cell fate choice > Methods > Flow cytometry and cell sorting
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“The oscillatory behavior may be a reflection of partial assembly of counter acting regulatory states or a lack of their stabilization.”
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“The Gfi1-GFP reporter expresses a stable GFP, which can over-estimate Gfi1 expression, likely accounting for higher GFP expression in GG3s (Fig. 3f) in spite of very low levels of Gfi1 transcripts (Fig. 3g).”
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In the paper
“Based on its frequency, this state is inferred to be metastable, but could be “trapped” by elimination of counter-acting determinants.”
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In the paper
“The concept of “trapping” of rare developmental intermediates by genetic perturbation is based on the analogy with trapping of unstable transition states in chemical reactions using physico-chemical strategies33.”
What the sources say
- Not checked yetcites [33] Mitchison, T. & Kirschner, M. Dynamic instability of microtubule growth. Nature 312, 237–242 (1984) source
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“Expression of Flt3, Csf1r and Cx3CR1 identifies MDP, while expression of Batf3 and Ifi205 suggests dendritic cell differentiation.”
§ Single-cell analysis of mixed-lineage states leading to a binary cell fate choice > Extended Data
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“Correlation between bulk RNA-Seq (TRUseq Stranded mRNA HT kit) for each sorted population compared to average single-cell gene expression values from the same sort (e.g. LSK, CMP, or GMP).”
§ Single-cell analysis of mixed-lineage states leading to a binary cell fate choice > Extended Data
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“In order to identify sub-populations of 3 cells, present at 10%, a minimum of 30 cells was required. For primary discovery analyses n>90 was required.”
§ Single-cell analysis of mixed-lineage states leading to a binary cell fate choice > Methods > RNA-Seq and ChIP-Seq Data Processing
Strength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✗ Based: failsProbably not a substantial claim. The paper’s conclusions may not depend on it. The paper may give no reason why it holds. It may state two separate claims as one.
In the paper
“In agreement with our previous report that Gfi1 controls two genetically separable programs; granulopoiesis and Hox-based myeloid progenitor proliferation25”
§ Single-cell analysis of mixed-lineage states leading to a binary cell fate choice > Extended Data
What the sources say
- Not checked yetcites [25] Holtschke, T. et al. Immunodeficiency and chronic myelogenous leukemia-like syndrome in mice with a targeted mutation of the ICSBP gene. Cell 87, 307–317 (1996) source
Strength:plain· not yet calibratedCausal:direct· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“This may generate oscillations in the regulatory states of multi- or bi-potential intermediates, resulting in bursts of alternate lineage gene expression.”
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Genes and cells outlined in the dotted box were re-clustered with HOPACH to delineate relationships between monocytic and granulocytic programming among the different indicated cell populations (Fig. 4c).”
§ Single-cell analysis of mixed-lineage states leading to a binary cell fate choice > Extended Data
Strength:plain· not yet calibratedCausal:correlational· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably not a substantial claim. The paper’s conclusions may not depend on it.
The paper relies on this without support.
The nature of the transition states manifested in vivo and the underlying dynamics of the cell-fate determinants have remained elusive.
“Although a bistable-gene-regulatory network has been proposed to regulate the specification of neutrophils versus macrophages7,8, the nature of the transition states manifested in vivo and the underlying dynamics of the cell-fate determinants have remained elusive.”
The paper relies on this without support.
Each ICGS-delineated cellular state has an underlying regulatory state characterized by distinct combinations of transcription factors.
“Each ICGS delineated cellular state is expected to have an underlying regulatory state characterized by distinct combinations of TFs.”
The paper relies on this without support.
Mixed-lineage states reflect molecular priming of developmental potentials by co-expressed alternate-lineage determinants, namely transcription factors.
“Such mixed-lineage states may reflect molecular priming of developmental potentials by co-expressed alternate-lineage determinants, namely transcription factors.”
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
In the citing paper
“As part of the genomics revolution, increasingly accurate deep sequencing and novel methods of cell tracking have revealed hematopoiesis to be more of a continuous and less of a discrete and punctuated process than originally envisioned.”
From Unraveling Hematopoiesis through the Lens of Genomics (2020) · cites this paper as Olsson et al., 2016
In the citing paper
“Thus, while the continuum model clearly addresses some of the oversimplifications of the classic model of hematopoiesis, gene expression alone may be insufficient to distinguish discrete cell populations and may miss other relevant aspects of biology (Olsson et al., 2016).”
From Unraveling Hematopoiesis through the Lens of Genomics (2020) · cites this paper as Olsson et al., 2016
In the citing paper
“When computationally reconstructed, single-cell RNA sequencing (scRNA-seq) profiling of the mammalian hematopoietic system typically produces a largely continuous distribution of cells from the earliest HSCs through the characteristic hematopoietic lineages (Nestorowa et al., 2016; Olsson et al., 2016; Paul et al., 2015; Pellin et al., 2019; Tusi et al., 2018; Weinreb et al., 2020).”
From Unraveling Hematopoiesis through the Lens of Genomics (2020) · cites this paper as Olsson et al., 2016
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
Each contradiction between two passages listed here was confirmed by an independent check.
Extended Data Figure 10 identifies panel i as both a CFU assay and an ICGS analysis
MinorThe caption says the cytospin examines CFU from panel i, but describes panel i as an ICGS analysis rather than a CFU assay.
“f, Cytospin analysis of eosinophils in GG1 derived CFU from panel i.”
“i, ICGS of GG1 and IG2 cells with those spanning the entire myeloid developmental spectrum (Fig. 1b).”
Extended Data Figure 7 assigns two different contents to panel c
MinorThe caption labels both a gene-expression comparison and ChIP-Seq/RNA-Seq tracks as panel c.
“c. Gfi1−/− GMPs show a significant increase in cell cycle-related gene expression compared to wildtype or Irf8−/− GMPs.”
“c, Gfi1 (green), Irf8 (blue) and Cebpα (red) ChIP-Seq and RNA-Seq tracks illustrating co-regulation at select loci.”
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Metadata
- Authors
- Olsson, A., Venkatasubramanian, M., Chaudhri, V. K., Aronow, B. J., Salomonis, N., Singh, H., & Grimes, H. L.
- Affiliations
- Cincinnati Children's Hospital Medical Center (US)
- Corresponding author
- Salomonis, Nathan
- Singh, Harinder
- Grimes, H. Leighton
- ORCID
- Venue
- Nature
- Published
- 2016-08-31
- Volume
- 537
- Issue
- 7622
- Pages
- 698-702
- ISSN
- 0028-0836 (ISSN-L)
- 1476-4687
- PMC
- PMC5161694
- OpenAlex
- W2515907404
- Semantic Scholar
- f4cfb99ea0ea2e73c36ba3b1cd57e59bd22bd1d3
- Cited by
- 476
- References
- 50
- Citations by year
- 2026: 11 · 2025: 24 · 2024: 42 · 2023: 44 · 2022: 38 · 2021: 55 · 2020: 78 · 2019: 64 · 2018: 58 · 2017: 59 · 2016: 3
- FWCI
- 31.89
- Type
- journal article
- Language
- en
- Update notices
- correction (2019-04-24)
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- Nature (publishedVersion)
- PubMed (publishedVersion)
- Europe PMC (PubMed Central) (acceptedVersion)
- PubMed Central (acceptedVersion)
- Funders
- National Institutes of Health (UL1TR001425)
- National Heart, Lung, and Blood Institute (R01HL122661)
- National Institute of Diabetes and Digestive and Kidney Diseases (P30DK090971, P30DK078392)
- National Institute of Arthritis and Musculoskeletal and Skin Diseases (P30AR047363)
- National Center for Advancing Translational Sciences
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Single-cell and spatial transcriptomics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gene Regulatory Network Analysis
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Genomics and Chromatin Dynamics
- Keywords
- cell fate determination, hematopoiesis, single-cell RNA sequencing, IRF8, GFI1, hematopoietic stem cells, myeloid progenitor cells, chromatin accessibility
- MeSH terms
- Interferon Regulatory Factor-8, Animals, Cell Differentiation, DNA-Binding Proteins, Female, Macrophages, Male, Mice, Inbred C57BL, Models, Biological, Neutrophils, Transcription Factors, Sequence Analysis, RNA, Cell Lineage, Gene Expression Profiling, Myeloid Progenitor Cells, Chromatin Immunoprecipitation, Interferon Regulatory Factors, Mice, Gene Regulatory Networks, Single-Cell Analysis
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