The DOI matches its Crossref record; 45 of the 45 references it cites were found in a registry or an index; 14 of the 23 sources its claims cite were checked for support.
Lineage tracing on transcriptional landscapes links state to fate during differentiation
Open accessPublished version of a preprint
Abstract
Mapping cell fate during hematopoiesis: Biologists have long attempted to understand how stem and progenitor cells in regenerating and embryonic tissues differentiate into mature cell types. Through the use of recent technical advances to sequence the genes expressed in thousands of individual cells, differentiation mechanisms are being revealed. Weinreb et al. extended these methods to track clones of cells (cell families) across time. Their approach reveals differences in cellular gene expression as cells progress through hematopoiesis, which is the process of blood production. Using machine learning, they tested how well gene expression measurements account for the choices that cells make. This work reveals that a considerable gap still exists in understanding differentiation mechanisms, and future methods are needed to fully understand—and ultimately control—cell differentiation. Science , this issue p. eaaw3381
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Claims the paper makes
64 Own result · 29 Own interpretation · 7 Own method · 5 No full text of the sources · 4 Supported · 4 Partly supported · 3 No basis given · 2 Not checked yet · accuracy not yet measured
Every source the paper cites for this claim was checked and supports it.
In the paper
“The strategy differs from CRISPR-based lineage tracing approaches (27, 28) in that it links states across time, not only at a single end-point.”
§ Lineage tracing on transcriptional landscapes links state to fate during differentiation > DISCUSSION
What the sources say
- Supportedcites [27] source
In Results > Tracing cell lineages during development (page unavailable), the authors say they isolated embryos at E8.5 or E9.5 for analysis and collected single-cell RNA-seq data from them. The Abstract and Discussion describe continuous CRISPR-based recording of lineage history, while Results > Simultaneous scRNA-seq to assign state describes assigning states to the sampled cells. This supports the contrast between recording lineage over time and measuring cellular states at an endpoint; the paper does not assess LARRY itself. (LLM, conf=0.85)
- Supportedcites [28] source
The Main text describes LINNAEUS as creating scars early in development, then sequencing scars and transcriptomes from dissociated cells at a later stage. Its reported single-cell analysis uses larvae at 5 days post fertilization, supporting the contrast with a method that links measured cell states across time. The source does not itself describe LARRY. (LLM, conf=0.88)
plaincorrelational- Asserted: passes — The authors put it forward as true.
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- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
- Supported
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
“Visualizing cells from uni-lineage clones revealed well-delineated domains of fate potential (Fig 2c).”
§ Lineage tracing on transcriptional landscapes links state to fate during differentiation > RESULTS > Clonal dynamics identify early transcriptional fate boundaries
boosted- Asserted: passes — The authors put it forward as true.
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- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
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
“In vitro, the cells defined a continuous state map spanning from multipotent progenitors (MPPs) to nine mature cell types that appeared in culture (Fig 1e,f): erythrocytes (Er), megakaryocytes (Mk), basophils (Ba), mast cells (Ma), eosinophils (Eos), neutrophils (Neu), monocytes (Mo), dendritic cells (plasmocytoid pDC; Ccr7+ migratory migDC) and lymphoid precursors (Ly).”
§ Lineage tracing on transcriptional landscapes links state to fate during differentiation > RESULTS > A simultaneous assay of clonal states and fates
plain- Asserted: passes — The authors put it forward as true.
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In the paper
“Many of these cell types were internally heterogeneous, with several types of DCs including CD11+, CD8+, migDC and pDC, as well as Ly6C+ classical and Ly6C- non-classical monocytes (Fig 1j; Supp Fig 3).”
§ Lineage tracing on transcriptional landscapes links state to fate during differentiation > RESULTS > A simultaneous assay of clonal states and fates
plain- Asserted: passes — The authors put it forward as true.
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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
“After profiling by scSeq, one or more barcodes could be robustly detected in 93% of GFP+ cells (Supp Fig 1a–c).”
§ Lineage tracing on transcriptional landscapes links state to fate during differentiation > RESULTS > A simultaneous assay of clonal states and fates
hedged- Asserted: passes — The authors put it forward as true.
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- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
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
“We retrieved 130,887 scSeq transcriptomes from culture and 182,173 single cells after transplantation (see Table 1 and Supp Methods 3 for details of analysis).”
§ Lineage tracing on transcriptional landscapes links state to fate during differentiation > RESULTS > A simultaneous assay of clonal states and fates
plain- Asserted: passes — The authors put it forward as true.
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- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
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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
“After transplantation, the cells again defined a continuous landscape spanning from MPPs through several stages of neutrophil maturation, as well as DCs, Mo, Er, B, T and Ba cells.”
§ Lineage tracing on transcriptional landscapes links state to fate during differentiation > RESULTS > A simultaneous assay of clonal states and fates
plain- Asserted: passes — The authors put it forward as true.
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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
“Specific barcode sequences overlapped rarely between replicate transduction experiments, at a frequency expected by chance for the library size (0.3% of 5000 barcodes appeared more than once).”
§ Lineage tracing on transcriptional landscapes links state to fate during differentiation > RESULTS > A simultaneous assay of clonal states and fates
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
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
“We retrieved 130,887 scSeq transcriptomes from culture and 182,173 single cells after transplantation (see Table 1 and Supp Methods 3 for details of analysis).”
§ Lineage tracing on transcriptional landscapes links state to fate during differentiation > RESULTS > A simultaneous assay of clonal states and fates
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
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
“they shared the same dominant fate 71% of the time (23% by chance).”
§ Lineage tracing on transcriptional landscapes links state to fate during differentiation > RESULTS > Functional purity of scSeq-defined cell states
plaincorrelational- Asserted: passes — The authors put it forward as true.
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- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
- It may state two separate claims as one. 36
- Probably not a substantial claim. 16
Every source the paper cites for this claim was checked and supports it.
In the paper
“Consistent with recent scSeq studies (19), progenitors with different fate potentials did not partition into discrete cell states, but instead formed a structured continuum.”
§ Lineage tracing on transcriptional landscapes links state to fate during differentiation > RESULTS > Clonal dynamics identify early transcriptional fate boundaries
What the sources say
- Supportedcites [19] source
The Results section ('Early haematopoiesis is a continuous process'; page unavailable) describes primitive HSCs and their immediate progeny as a continuum rather than discrete populations. The Discussion says these cells gradually acquire lineage priming in multiple directions, supporting the description of a structured continuum. The finding concerns early HSPCs; the paper says more differentiated progenitors form discrete populations. (LLM, conf=0.88)
plain- Asserted: passes — The authors put it forward as true.
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It may state two separate claims as one.
- Supported
Every source the paper cites for this claim was checked and supports it.
In the paper
“In addition, the high-dimensional nature of scSeq allows more than one approach to constructing cell state trajectories from the same data (4).”
§ Lineage tracing on transcriptional landscapes links state to fate during differentiation > Introduction
What the sources say
- Supportedcites [4] source
The introduction describes multiple methods for reconstructing trajectories from high-dimensional single-cell snapshots. The section “Multiple Dynamic Trajectories Can Generate the Same High-Dimensional” says that dynamic inference has no unique solution, supporting the claim that the same data can yield more than one trajectory. Page numbers are unavailable in our copy. (LLM, conf=0.90)
plaindirect- Asserted: passes — The authors put it forward as true.
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- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
- Supported
The cited sources support only part of this claim — read each source’s verdict below.
In the paper
“scSeq captures mature cell types alongside all stages of cell differentiation, revealing a ‘state map’ in gene expression space.”
§ Lineage tracing on transcriptional landscapes links state to fate during differentiation > Introduction
What the sources say
- Partly supportedcites [3] sourceExperimental — accuracy not yet measured
In “Single-Cell Experimental Technologies to Interrogate Cell States From Tissues” (page unavailable), the paper says scRNA-seq measures gene expression and describes cell states as positions in a molecular-marker data space. It also says transitional states can be sampled to infer a continuum, but they are often rare and require dense sampling; it does not support the claim that sequencing captures every differentiation stage. (LLM, conf=0.85)
- Partly supportedcites [4] sourceExperimental — accuracy not yet measured
The paper’s discussion of genome-scale single-cell measurements says scRNA-seq can generate an atlas of tissue cell states, and its hematopoiesis analysis describes a continuum of expression states at various stages of maturity. It does not say scRNA-seq captures every differentiation stage or necessarily includes mature cell types; the mapped endpoints in that analysis are identified as progenitors. No page numbers are available in our copy. (LLM, conf=0.87)
- Not checked — no full text of the sourcecites [5] source
- Partly supportedcites [6] sourceExperimental — accuracy not yet measured
Figure 3 (Abstract; page unavailable) shows a gene-expression embedding containing major transient and mature cell subpopulations, supporting the idea of a state map. The paper does not establish that single-cell RNA sequencing captures *all* stages of differentiation; its abstract also describes each measurement as a static snapshot. (LLM, conf=0.84)
- Not checked — no full text of the sourcecites [7] source
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
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It may state two separate claims as one.
- Partly supported
The cited sources support only part of this claim — read each source’s verdict below.
In the paper
“The gold standard for inferring the relationship between progenitors and their offspring is lineage tracing, where a subset of progenitors is labeled, typically using genetic approaches that mark cells expressing defined marker genes, and their fate is profiled at a later time point (1).”
§ Lineage tracing on transcriptional landscapes links state to fate during differentiation > Introduction
What the sources say
- Partly supportedcites [1] sourceExperimental — accuracy not yet measured
The Abstract describes fate maps as important tools for defining relationships between embryonic and later tissue structure, and Section 2 explains how genetic labeling tracks descendants over time. The paper supports the use of lineage tracing for this purpose, but does not establish the citing sentence’s stronger claim that it is the gold standard. (LLM, conf=0.82)
boosted- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
- Partly supported
The cited sources support only part of this claim — read each source’s verdict below.
In the paper
“But recent state maps from scSeq (9), as well as clonal studies using barcodes (10) and single cell culture (11), suggest that the traditional intermediate cell types are internally heterogeneous in state and fate potential, with HSPCs lying along a continuum of states rather than a stepwise hierarchy.”
§ Lineage tracing on transcriptional landscapes links state to fate during differentiation > Introduction
What the sources say
- Supportedcites [9] source
Split into 2 atoms — 2 confirmed, 0 partially supported, 0 disputed, 0 misattributed, 0 unverifiable. Expand the claim to see per-atom verdicts.
- Partly supportedcites [10] sourceExperimental — accuracy not yet measured
Split into 2 atoms — 1 confirmed, 1 partially supported, 0 disputed, 0 misattributed, 0 unverifiable. Expand the claim to see per-atom verdicts.
- Partly supportedcites [11] sourceExperimental — accuracy not yet measured
Split into 2 atoms — 1 confirmed, 1 partially supported, 0 disputed, 0 misattributed, 0 unverifiable. Expand the claim to see per-atom verdicts.
hedgedcorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
- Supported
The cited sources support only part of this claim — read each source’s verdict below.
In the paper
“For cells undergoing multi-lineage fate choice, scSeq has been used to estimate lineage hierarchy based on the assumption that cell types with transcriptionally similar differentiation pathways are clonally related (3–5, 7).”
§ Lineage tracing on transcriptional landscapes links state to fate during differentiation > RESULTS > Agreement of state and clonal differentiation hierarchies
What the sources say
- Partly supportedcites [3] sourceExperimental — accuracy not yet measured
The ‘Downstream Analysis of Reconstructed Trajectories’ section (page unavailable) says that single-cell trajectories can indicate branching cell-fate decisions and hierarchical relationships. But ‘Notes on Using and Evaluating Trajectory Reconstruction Algorithms’ (page unavailable) distinguishes inferred cell-state transitions from parent–child lineage, which requires experimental validation; it does not establish that transcriptionally similar pathways imply clonal origin. (LLM, conf=0.85)
- Partly supportedcites [4] sourceExperimental — accuracy not yet measured
The introduction says earlier single-cell methods fitted curves or trees to cell-state distributions to reconstruct differentiation trajectories, including branching ones. It does not say those studies assumed transcriptionally similar pathways imply clonal relatedness; that specific assumption is unsupported by our copy. (LLM, conf=0.82)
- Not checked — no full text of the sourcecites [5] source
- Not checked — no full text of the sourcecites [7] source
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim.
- Partly supported
Every source the paper cites for this claim was checked and supports it.
In the paper
“This reasoning reflects a formal result known as the Data Processing Inequality (20) (Supp Methods 10.1).”
§ Lineage tracing on transcriptional landscapes links state to fate during differentiation > RESULTS > Functional purity of scSeq-defined cell states
What the sources say
- Supportedcites [20] source
The Self-consistent equitability section (p. 6) defines the Data Processing Inequality as a formal condition and explains that it captures the intuition that information is lost, not gained, through a noisy channel. This supports the cited sentence’s characterization of the reasoning as reflecting DPI. (LLM, conf=0.91)
plain- Asserted: passes — The authors put it forward as true.
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Probably not a substantial claim.
- Supported
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
“On this landscape clones exhibited a range of behaviors including uni-lineage and multi-lineage differentiation, and self-renewal of early progenitors (Fig 1g).”
§ Lineage tracing on transcriptional landscapes links state to fate during differentiation > RESULTS > A simultaneous assay of clonal states and fates
plain- Asserted: passes — The authors put it forward as true.
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- Load-bearing: passes — The paper’s conclusions depend on it.
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It may state two separate claims as one.
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
“In these two experiments, 38% and 63% of cells respectively belonged to a clone of two or more cells (5864 and 7751 clones), with 1,816 and 817 clones in total spanning early and late timepoints (Fig 1d,i).”
§ Lineage tracing on transcriptional landscapes links state to fate during differentiation > RESULTS > A simultaneous assay of clonal states and fates
plain- Asserted: passes — The authors put it forward as true.
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- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
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
“We found that pairs of sisters profiled on day 2 localized in the SPRING graph, had correlated gene expression (median R=0.846) and that a majority (70%) fell in the same or nearest neighbor cluster (Fig 2a,b; Supp Fig 4a–d; Supp Methods section 5).”
§ Lineage tracing on transcriptional landscapes links state to fate during differentiation > RESULTS > Clonal dynamics identify early transcriptional fate boundaries
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
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
“In these two experiments, 38% and 63% of cells respectively belonged to a clone of two or more cells (5864 and 7751 clones), with 1,816 and 817 clones in total spanning early and late timepoints (Fig 1d,i).”
§ Lineage tracing on transcriptional landscapes links state to fate during differentiation > RESULTS > A simultaneous assay of clonal states and fates
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
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
“Where the progenitors for different fates overlapped, we observed bi-potent or oligopotent clones, indicating the location of fate commitment boundaries (Fig 2d).”
§ Lineage tracing on transcriptional landscapes links state to fate during differentiation > RESULTS > Clonal dynamics identify early transcriptional fate boundaries
hedgedcorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
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
“In these two experiments, 38% and 63% of cells respectively belonged to a clone of two or more cells (5864 and 7751 clones), with 1,816 and 817 clones in total spanning early and late timepoints (Fig 1d,i).”
§ Lineage tracing on transcriptional landscapes links state to fate during differentiation > RESULTS > A simultaneous assay of clonal states and fates
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
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
“A minority of cells, however, were more diverged, with 10% falling outside a four-cluster radius (compared to 80% for random cell pairs).”
§ Lineage tracing on transcriptional landscapes links state to fate during differentiation > RESULTS > Clonal dynamics identify early transcriptional fate boundaries
plain- Asserted: passes — The authors put it forward as true.
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- Load-bearing: passes — The paper’s conclusions depend on it.
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It may state two separate claims as one.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“We did not detect megakaryocytes, possibly because they did not survive bone marrow harvest, flow-sorting and single-cell encapsulation intact.”
§ Lineage tracing on transcriptional landscapes links state to fate during differentiation > RESULTS > A simultaneous assay of clonal states and fates
hedgedconditional- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
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
“In the data, monocytes appeared to form a spectrum from neutrophil-like to DC-like, expressing alternatively neutrophil elastase (Elane) and other neutrophil markers, or MHC class II components (Cd74 and H2-Aa) (Fig 4a).”
§ Lineage tracing on transcriptional landscapes links state to fate during differentiation > RESULTS > Distinct routes of monocyte differentiation
hedgedcorrelational- Asserted: passes — The authors put it forward as true.
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It may state two separate claims as one.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“We demonstrated that this strategy can be simply extended to paired perturbation experiments that compare sister cells treated in different states.”
§ Lineage tracing on transcriptional landscapes links state to fate during differentiation > DISCUSSION
boosted- Asserted: passes — The authors put it forward as true.
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- Load-bearing: fails — The paper’s conclusions may not depend on it.
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Probably not a substantial claim. The paper’s conclusions may not depend on it.
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
“Across all conditions, the highest overall predictive accuracy from transcriptional state was 60% in vitro and 51% in vivo.”
§ Lineage tracing on transcriptional landscapes links state to fate during differentiation > RESULTS > How predictable is cell fate from gene expression?
plaincorrelational- Asserted: passes — The authors put it forward as true.
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- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
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
“As in vitro, the DC-related monocytes were enriched for DC marker genes, whereas neutrophil-related monocytes were enriched for neutrophil markers (Fig 4g,h).”
§ Lineage tracing on transcriptional landscapes links state to fate during differentiation > RESULTS > Distinct routes of monocyte differentiation
plaincorrelational- Asserted: passes — The authors put it forward as true.
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- Load-bearing: passes — The paper’s conclusions depend on it.
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It may state two separate claims as one.
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
“with the most accurate algorithms achieving late prediction accuracy of 76% in vitro and 70% in vivo, compared to 60% and 52% respectively for early prediction.”
§ Lineage tracing on transcriptional landscapes links state to fate during differentiation > RESULTS > Functional purity of scSeq-defined cell states
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
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
“The multipotent progenitor (CD34+) fraction of day 2 cells (Fig 2e) contained several broad domains, including a restricted central domain of stem cell marker (Procr) expression, a wing expressing Gata2 – an erythroid and stem cell marker – and an opposing wing expressing Flt3 indicative of lymphoid priming.”
§ Lineage tracing on transcriptional landscapes links state to fate during differentiation > RESULTS > Clonal dynamics identify early transcriptional fate boundaries
plain- Asserted: passes — The authors put it forward as true.
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- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
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
“For example, Ikaros family zinc finger 2 (Ikzf2) – a myeloid leukemia gene not previously associated with fate choice – was enriched in eosinophil and mast cell progenitors, but not basophil or megakaryocyte.”
§ Lineage tracing on transcriptional landscapes links state to fate during differentiation > RESULTS > Clonal dynamics identify early transcriptional fate boundaries
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
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
“Transcription factors were only marginally more informative than random size-matched gene sets (10% more informative in vitro; 3% more informative in vivo), whereas differentially expressed genes were substantially more informative (38% more informative in vitro; 20% more informative in vivo).”
§ Lineage tracing on transcriptional landscapes links state to fate during differentiation > RESULTS > How predictable is cell fate from gene expression?
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
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
“Second, we analyzed classical monocytes (Supp Fig 10g) and human peripheral blood monocytes (Supp Fig 10h) by scSeq. Principal component analysis shows that in both cases a spectrum exists of neutrophil-like to DC-like gene expression (see Table 8 for differentially expressed genes), which is also evident in the expression of marker genes (Fig 4i).”
§ Lineage tracing on transcriptional landscapes links state to fate during differentiation > RESULTS > Distinct routes of monocyte differentiation
plaincorrelational- Asserted: passes — The authors put it forward as true.
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- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
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
“Megakaryocyte, basophil, mast cell and eosinophil potential were all restricted to the Gata2+ region, yet derived from separate subsets within this region.”
§ Lineage tracing on transcriptional landscapes links state to fate during differentiation > RESULTS > Clonal dynamics identify early transcriptional fate boundaries
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
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In the paper
“As before the cells spanned a continuous landscape with domains of primed gene expression, including a central domain of stem cell (Procr) and opposing wings of Gata2 and Flt3 expression (Fig 2h), that correlated with output into the nine respective post-transplant fates (Fig 2i).”
§ Lineage tracing on transcriptional landscapes links state to fate during differentiation > RESULTS > Clonal dynamics identify early transcriptional fate boundaries
plaincorrelational- Asserted: passes — The authors put it forward as true.
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In the paper
“but in the early progenitor (Cd34+) region, clonal tracking revealed a bifurcation of monocyte and neutrophil potential that was generally not detected by the prediction algorithms, although FateID performed slightly better (Fig 5c,d; R < 0.26 for all methods).”
§ Lineage tracing on transcriptional landscapes links state to fate during differentiation > RESULTS > scSeq-based models do not fully predict fate choice
plain- Asserted: passes — The authors put it forward as true.
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In the paper
“Cell culture and transplantation studies over several decades have led to the prevailing model of hematopoiesis as a branching hierarchy with defined fate-restricted intermediates (8).”
§ Lineage tracing on transcriptional landscapes links state to fate during differentiation > Introduction
What the sources say
- Not checked — no full text of the sourcecites [8] source
plain- Asserted: passes — The authors put it forward as true.
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Probably not a substantial claim.
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“These tests justified approximating single-cell trajectories by clonal trajectories, though with some loss in resolution of fate boundaries expected due to ~10% diverged sister pairs.”
§ Lineage tracing on transcriptional landscapes links state to fate during differentiation > RESULTS > Clonal dynamics identify early transcriptional fate boundaries
plaindirect- Asserted: passes — The authors put it forward as true.
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In the paper
“scSeq data does not directly report the stages at which progenitor cells become committed to one or more fates or how many distinct paths might lead cells to the same end states.”
§ Lineage tracing on transcriptional landscapes links state to fate during differentiation > Introduction
plain- Asserted: passes — The authors put it forward as true.
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In the paper
“LARRY defines a scSeq-compatible lineage tracing approach that links cell states to clonal fates simultaneously from multiple initial conditions, without needing to target each specific progenitor state.”
§ Lineage tracing on transcriptional landscapes links state to fate during differentiation > DISCUSSION
plaincorrelational- Asserted: passes — The authors put it forward as true.
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In the paper
“This analysis agrees with earlier observations (21).”
§ Lineage tracing on transcriptional landscapes links state to fate during differentiation > RESULTS > Distinct routes of monocyte differentiation
What the sources say
- Not checked — no full text of the sourcecites [21] source
plain- Asserted: passes — The authors put it forward as true.
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Probably not a substantial claim. The paper’s conclusions may not depend on it.
In the paper
“These results are consistent with a recent finding that immunophenotypically-defined monocyte-dendritic progenitors (MDPs) and granulocyte-monocyte progenitors (GMPs) give rise to monocytes with DC-like and neutrophil-like characteristics, respectively (21).”
§ Lineage tracing on transcriptional landscapes links state to fate during differentiation > RESULTS > Distinct routes of monocyte differentiation
What the sources say
- Not checked — no full text of the sourcecites [21] source
plaindirect- Asserted: passes — The authors put it forward as true.
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Probably not a substantial claim.
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
“and that cells differentiate via a continuous, structured fate hierarchy that differs from classical tree-like depictions of hematopoiesis in its clonal structure.”
§ Lineage tracing on transcriptional landscapes links state to fate during differentiation > DISCUSSION
plaindirect- Asserted: passes — The authors put it forward as true.
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In the paper
“These results suggest that the predictive content of our gene expression measurements in HSPCs is almost entirely contained within several hundred differentially expressed genes, and only marginally enriched in transcription factors.”
§ Lineage tracing on transcriptional landscapes links state to fate during differentiation > RESULTS > How predictable is cell fate from gene expression?
hedgedcorrelational- Asserted: passes — The authors put it forward as true.
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In the paper
“Further, bipotent domains formed extended fate boundaries, indicating that differentiation progression can occur independently of fate commitment over some time.”
§ Lineage tracing on transcriptional landscapes links state to fate during differentiation > RESULTS > Clonal dynamics identify early transcriptional fate boundaries
hedgedconditional- Asserted: passes — The authors put it forward as true.
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In the paper
“Overall, these observations support the view that functional lineage priming varies across a continuous hematopoietic progenitor landscape and covaries with the heterogenous expression of genes, including transcription factors and a wide array of other functional gene categories.”
§ Lineage tracing on transcriptional landscapes links state to fate during differentiation > RESULTS > Clonal dynamics identify early transcriptional fate boundaries
plaincorrelational- Asserted: passes — The authors put it forward as true.
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In the paper
“Thus, the DC-like and neutrophil-like trajectories observed here likely represent MDP and GMP pathways of monocyte differentiation, and they clarify the location of these states in a gene expression continuum.”
§ Lineage tracing on transcriptional landscapes links state to fate during differentiation > RESULTS > Distinct routes of monocyte differentiation
hedgedcorrelational- Asserted: passes — The authors put it forward as true.
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In the paper
“As with CRISPR-based approaches, the method cannot study processes faster than one cell cycle.”
§ Lineage tracing on transcriptional landscapes links state to fate during differentiation > DISCUSSION
plain- Asserted: passes — The authors put it forward as true.
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Probably not a substantial claim. The paper’s conclusions may not depend on it.
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
“which could be explained by cells remaining in the MPP state for a variable duration before initiating neutrophil differentiation.”
§ Lineage tracing on transcriptional landscapes links state to fate during differentiation > RESULTS > Temporal progression is captured by pseudotime
hedgedconditional- Asserted: passes — The authors put it forward as true.
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Probably not a substantial claim. The paper’s conclusions may not depend on it.
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
“Our data therefore contains two different pathways of monocyte differentiation with distinct clonal relationships and gene expression dynamics.”
§ Lineage tracing on transcriptional landscapes links state to fate during differentiation > RESULTS > Distinct routes of monocyte differentiation
plaincorrelational- Asserted: passes — The authors put it forward as true.
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In the paper
“Despite the less mature state of these cells, each fate outcome correlated with unique enriched genes prior to transplantation (Fig 2j; n=190 [173 unique] differentially expressed genes at FDR=0.05; Table 3), indicating a surprising degree of specific priming at this early stage of differentiation.”
§ Lineage tracing on transcriptional landscapes links state to fate during differentiation > RESULTS > Clonal dynamics identify early transcriptional fate boundaries
hedgedcorrelationaln = 190- Asserted: passes — The authors put it forward as true.
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What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“and it does not require delivering multiple components.”
§ Lineage tracing on transcriptional landscapes links state to fate during differentiation > DISCUSSION
plain- Asserted: passes — The authors put it forward as true.
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Probably not a substantial claim. The paper’s conclusions may not depend on it.
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In the paper
“These results provide a framework for comparing computational models of differentiation, and may serve as a useful benchmark for improving them.”
§ Lineage tracing on transcriptional landscapes links state to fate during differentiation > RESULTS > scSeq-based models do not fully predict fate choice
hedgedconditional- Asserted: passes — The authors put it forward as true.
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It may state two separate claims as one.
- Rests on an assumption: A shortage of ground-truth data has made it difficult to compare and validate current methods for modeling cell-state dynamics.
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
“These improvements in accuracy for late prediction reflect the high rate of concordance between sisters cell fates, and hold for clones of all potencies (Fig 3d)”
§ Lineage tracing on transcriptional landscapes links state to fate during differentiation > RESULTS > Functional purity of scSeq-defined cell states
plaindirect- Asserted: passes — The authors put it forward as true.
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In the paper
“Yet this assumption may not always hold: similar end states could also arise from non-overlapping clones (26) and distant end states could share lineage through asymmetric division.”
§ Lineage tracing on transcriptional landscapes links state to fate during differentiation > RESULTS > Agreement of state and clonal differentiation hierarchies
What the sources say
- Not checked — no full text of the sourcecites [26] source
hedged- Asserted: passes — The authors put it forward as true.
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Probably 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
“The distinct clonal origins of monocytes suggested that they arise from progenitors with different fate potentials, and possibly different gene expression.”
§ Lineage tracing on transcriptional landscapes links state to fate during differentiation > RESULTS > Distinct routes of monocyte differentiation
hedgedcorrelational- Asserted: passes — The authors put it forward as true.
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None of the sources cited for this claim have been checked yet.
In the paper
“If there were no hidden variables, we reasoned that the information shared between separated sister cells could only decrease as time passes.”
§ Lineage tracing on transcriptional landscapes links state to fate during differentiation > RESULTS > Functional purity of scSeq-defined cell states
hedgeddirect- Asserted: passes — The authors put it forward as true.
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Probably not a substantial claim.
None of the sources cited for this claim have been checked yet.
In the paper
“Conversely, if there are stable properties that influence cell fate but are hidden from scSeq, then the mutual information between sisters could increase over time as these properties manifest in cell fate.”
§ Lineage tracing on transcriptional landscapes links state to fate during differentiation > RESULTS > Functional purity of scSeq-defined cell states
hedgedconditional- Asserted: fails — The authors may not put it forward as true.
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Probably not a substantial claim. The authors may not put it forward as true.
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
“These results support the use of pseudotime methods for mapping differentiation progression.”
§ Lineage tracing on transcriptional landscapes links state to fate during differentiation > RESULTS > Temporal progression is captured by pseudotime
plain- Asserted: passes — The authors put it forward as true.
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Probably not a substantial claim.
The authors state this as an assumption.
If no hidden variables exist, the two day-6 fates are independent once the day-2 state is conditioned on.
“Without hidden variables, the two fates observed at day 6 should be statistically independent after conditioning on the day 2 state.”
The paper relies on this without support.
A shortage of ground-truth data has made it difficult to compare and validate current methods for modeling cell-state dynamics.
“Due to a lack of ground truth data, existing methods have been hard to compare and validate.”
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?
The source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“We adapted culture conditions37,39 that permit the parallel differentiation of primary mouse HSCs into red blood cells, megakaryocytes, neutrophils, eosinophils, basophils, monocytes and dendritic cells.”
From Design principles of cell-state-specific enhancers in hematopoiesis (2025) · cites this paper as [37]
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“all primary hematopoietic cells activate Trp53 upon lentiviral infection87 without affecting the differentiation of cells in the ex vivo system used here.37,39”
From Design principles of cell-state-specific enhancers in hematopoiesis (2025) · cites this paper as [37]
Experimental — accuracy not yet measuredThe cited source says what the claim attributes to it.
In the citing paper
“Advanced primary cell models of hematopoietic differentiation37,38,39 provide a unique opportunity to study this broadly relevant question, as they allow for measuring the activity of a large number of synthetic DNA constructs in a complex model of stemness and differentiation.”
From Design principles of cell-state-specific enhancers in hematopoiesis (2025) · cites this paper as [37]
In the citing paper
“PCA-batch-correction was performed using Patient 4 as a reference, as previously described (93).”
From Non-neuronal expression of SARS-CoV-2 entry genes in the olfactory system suggests mechanisms underlying COVID-19-associated anosmia (2020) · cites this paper as [93]
In the citing paper
“Further complicating the picture of hematopoiesis, it appears that there are multiple differentiation paths that cells can follow to produce the same lineage, such as is observed for monocyte differentiation (Figure 2) (Weinreb et al., 2020).”
From Unraveling Hematopoiesis through the Lens of Genomics (2020) · cites this paper as Weinreb et al., 2020
In the citing paper
“While cell-sorting based methods often show a multipotent HSC and progenitor compartment that gives rise to unipotent lineage-committed cells (Notta et al., 2016), the combination of scRNA-seq and cell barcoding has now directly demonstrated that the early fate biases in hematopoietic stem and progenitor cells (HSPCs) significantly impact eventual fate (Weinreb et al., 2020).”
From Unraveling Hematopoiesis through the Lens of Genomics (2020) · cites this paper as Weinreb et al., 2020
Citations
Cites
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“Consistent with recent scSeq studies (19), progenitors with different fate potentials did not partition into discrete cell states, but instead formed a structured continuum.”
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“However, this assumption may not always hold: Similar end states could also arise from nonoverlapping clones (26), and distant end states could share lineage through asymmetric division.”
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Cited by
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Items 401–500 of 723
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InfluentialA unified multimodal single-cell framework reveals a discrete state model of hematopoiesis in mice
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“Recent studies have provided proof-of-concept use of ‘expressed’ DNA barcodes in detecting clones in bulk (35) or single cell RNA sequences (36).”
BackgroundBdLT-Seq as a barcode decay-based method to unravel lineage-linked transcriptome plasticity
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BackgroundQuantifying Cellular Pluripotency and Pathway Robustness Through Forman-Ricci Curvature
Murgas, K. A., Saucan, E., & Sandhu, R. (2021) · Studies in computational intelligence · doi:10.1007/978-3-030-93413-2_51 · Cited by 8
Pak, B., Schmitt, C. E., Choi, W., Kim, J., Han, O., Alsiö, J. M., Jung, D., Williams, D. R., Coppieters, W. R., Stainier, D. Y. R., et al. (2020) · Frontiers in Cell and Developmental Biology · doi:10.3389/fcell.2020.589717 · Cited by 5
“…including the somites, cephalic mesoderm, and tailbud can serve as additional sources for ECs to accommodate the rapid expansion of the embryo during somitogenesis (Couly et al., 1995; Wilting et al., 1995; Pouget et al., 2008; Martin and Kimelman, 2012; Nguyen et al., 2014; Row et al., 2016).”
BackgroundCellular identity through the lens of direct lineage reprogramming
Falk, S., Han, D., & Karow, M. (2021) · Current Opinion in Genetics & Development · doi:10.1016/j.gde.2021.06.015 · Cited by 5
Yeo, G. H. T., Saksena, S. D., & Gifford, D. K. (2020) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2020.08.26.269332 · Cited by 5
A Meta-Analytic Single-Cell Atlas of Mouse Bone Marrow Hematopoietic DevelopmentPreprint
Harris, B. D., Lee, J., & Gillis, J. (2021) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2021.08.12.456098 · Cited by 5
“Projecting individual datasets into a latent space using UMAP for Weinreb et al 2020 and Rodriguez-Fraticelli et al 2020 clearly shows technical variation between annotated batches, while Tikhonova et al 2019, despite annotating multiple batches, does not present strong batch effects (Supplementary…”
MethodLearning Single-Cell Perturbation Responses using Neural Optimal TransportPreprint
Bunne, C., Stark, S. G., Gut, G., del Castillo, J. S., Levesque, M. P., Lehmann, K.-V., Pelkmans, L., Krause, A., Rätsch, G., & * (2022) · Research Square · doi:10.21203/rs.3.rs-1805107/v1 · Cited by 5
Bozhilov, Y. K., Brown, E., Hsu, I., Singh, I., Rodriguez-Fraticelli, A., Roy, A., Yamazaki, S., & Wilkinson, A. C. (2024) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2024.09.17.613552 · Cited by 6
Self-citationComprehensive Lineage Tracing Maps the Landscape of Cell Fate Decisions in Mouse EmbryogenesisPreprint
Colgan, W. N., Koblan, L. W., Villagrana, J., et al. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.05.07.722278 · Cited by 5
-Omics approaches to study and model cell-cell interactions in engineered tissues
Stark, C., Jenison, S. E., & Ngo, M. T. (2025) · Frontiers in Chemical Engineering · doi:10.3389/fceng.2025.1629455 · Cited by 7
“Similarly, Weinreb et al. applied scRNA-seq with lineage tracing to hematopoiesis, uncovering how transcriptional landscapes evolve as progenitors commit to speci fi c blood lineages (Weinreb et al., 2020).”
Inge, O. C. K., Copin, E., Cornwall-Scoones, J., Gharibi, B., Rodríguez‐Hernández, I., Soro-Barrio, P., Strom, M., Chakravarty, P., Briscoe, J., & Santos, S. D. M. (2025) · Developmental Cell · doi:10.1016/j.devcel.2025.08.009 · Cited by 6
Xu, Y., Kapanadze, T. G., Gaedcke, S., Jirmo, A. C., Sablotny, S., Schroth, F. N., Hille, S. S., Müller, O. J., Lochner, M., Haller, H. G., et al. (2025) · iScience · doi:10.1016/j.isci.2025.113795 · Cited by 4
“20,21 The heterogeneity of inflammatory monocytes further includes Ym1 + Ly6C hi or Cxcl10 monocytes, which expand under pathologic conditions.”
InfluentialClonal dynamics shaped by diverse drug-tolerant persister states in melanoma resistance
Li, H., Chen, Y., Kaster, J., Dunne, M., Xiao, M., Li, L., Thomas, M., Promi, N., Fingerman, D., Brown, G. S., et al. (2026) · Molecular Cancer · doi:10.1186/s12943-026-02622-9 · Cited by 5
“Static barcoding strategies such as LARRY and CellTag allow clonal labeling that can be read out by single-cell RNA sequencing (scRNA-seq) [9, 10], and the Watermelon system further links clonal identity with cell state by combining lentiviral barcodes, fluorescent proliferation reporters, and…”
Wang, S., Xiao, R., Chen, Y., Ye, Y., He, T., Yang, Y., Chen, X., & Chou, C. (2025) · Journal of Leukocyte Biology · doi:10.1093/jleuko/qiaf026 · Cited by 4
Guo, W., Chen, Z., Li, X., Huang, J., Hu, Q., & Gu, J. (2025) · Cell Systems · doi:10.1016/j.cels.2025.101398 · Cited by 4
Lan, T., Hutvágner, G., Zhang, X., Liu, T., Wong, L., & Li, J. (2022) · Nucleic Acids Research · doi:10.1093/nar/gkac785 · Cited by 4
“Cell trajectory inference is essential for a variety of research areas related to molecular function annotation (3–5), cell lineage identification (6,7), intra-tumor heterogeneity analysis (8), and cancer progression understanding (9,10).”
BackgroundAn image-guided microfluidic system for single-cell lineage tracking
Kamil, M. A., Fourneaux, C., Yılmaz, A., Stavrakis, S., Parmentier, R., Páldi, A., Gonin-Giraud, S., deMello, A. J., & Gandrillon, O. (2023) · PLoS ONE · doi:10.1371/journal.pone.0288655 · Cited by 4
Ganuza, M., Morales‐Hernández, A., Huizen, A. V. V., Chabot, A., Hall, T., Caprio, C., Finkelstein, D., Kilimann, M. W., & McKinney‐Freeman, S. (2024) · Blood Advances · doi:10.1182/bloodadvances.2023012426 · Cited by 4
“…transplantation imposes additional regenerative stress on HSPCs and only HSPCs capable of preserving self-renewal potential after primary transplantation persist to repopulate secondary recipients, 28,36-40 these suggest a potential role for Nbea in the maintenance of durable self-renewing HSCs.”
BackgroundUnderstanding rheumatic disease through continuous cell state analysis
Marshall, L. M., Raychaudhuri, S., & Viatte, S. (2025) · Nature Reviews Rheumatology · doi:10.1038/s41584-025-01253-6 · Cited by 5
“Ultimately, experimental validation, such as lineage tracing 117 , is necessary to substantiate these relationships.”
Heterogeneity of monocytes in cancer
Zhang, X. (2025) · American Journal of Cancer Research · doi:10.62347/cutm4281 · Cited by 4
Peterson, J. C., Kelder, T. P., Goumans, M., Jongbloed, M. R. M., & DeRuiter, M. C. (2021) · Journal of Cardiovascular Development and Disease · doi:10.3390/jcdd8050047 · Cited by 4
Leimkühler, N. B., Costa, I. G., & Schneider, R. K. (2021) · Experimental Hematology · doi:10.1016/j.exphem.2021.09.006 · Cited by 4
“Single-cell techniques and transcriptomics have radically changed our view of cellular identity and function [9].”
BackgroundThe cancer epigenome: Non‐cell autonomous player in tumor immunity
Kato, S., Maeda, Y., Sugiyama, D., Watanabe, K., Nishikawa, H., & Hinohara, K. (2022) · Cancer Science · doi:10.1111/cas.15681 · Cited by 3
Lineage tracing reveals fate bias and transcriptional memory in human B cells
Swift, M. A., Horns, F., & Quake, S. R. (2023) · Life Science Alliance · doi:10.26508/lsa.202201792 · Cited by 5
“This combination directly measures the molecular relationships between progenitors and their offspring, allowing stronger inference of molecular determinants of cell fates (Biddy et al, 2018; Ludwig et al, 2019; Weinreb et al, 2020).”
Contributions of transcriptional noise to leukaemia evolution: KAT2A as a case-study
Pina, C. (2024) · Philosophical Transactions of the Royal Society B Biological Sciences · doi:10.1098/rstb.2023.0052 · Cited by 6
“Differentiation between the two pre-leukaemia progression scenarios requires cell barcoding experiments [111,112] to trace the trajectory and genetic composition of individual cells.”
BackgroundPrzytycki, P. F., & Pollard, K. S. (2024) · Genome biology · doi:10.1186/s13059-024-03440-2 · Cited by 3
“Examples include a cell type tree of prenatal mouse development [25], C. elegans embryogenesis [26], hematopoiesis [27], and D. melanogaster embryogenesis [28].”
Genetically Encoded Fluorescence Barcodes Allow for Single-Cell Analysis via Spectral Flow Cytometry
Lu, X., Pritko, D. J., Abravanel, M., Huggins, J. R., Ogunleye, O., Biswas, T., Ashy, K. C., Woods, S. K., Livingston, M. W. T., Blenner, M., et al. (2025) · ACS Synthetic Biology · doi:10.1021/acssynbio.4c00807 · Cited by 5
“38 Fluorescent barcodes are usually nondestructive/fast/inexpensive to readout via flow cytometry or microscopy.”
Distributed and centralized control during differentiation
Chubb, J. R., Ford, H. Z., & Antolović, V. (2021) · Developmental Cell · doi:10.1016/j.devcel.2021.07.007 · Cited by 4
Inference of high-resolution trajectories in single cell RNA-Seq data from RNA velocityPreprint
Zhang, Z., & Zhang, X. (2020) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2020.09.30.321125 · Cited by 6
Wei, C., Dong, L., Xiong, S., Tang, Y., Yu, P., Liu, W., & Cheng, L. (2023) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2023.04.28.538645 · Cited by 4
Decoding Heterogenous Single-cell Perturbation ResponsesPreprint
Song, B., Liu, D., Dai, W., McMyn, N. F., Wang, Q., Yang, D., Krejčí, A., Vasilyev, A., Untermoser, N., Loregger, A., et al. (2023) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2023.10.30.564796 · Cited by 4
“A mixture of cells from different perturbations can be sequenced at the same time, and the identity of cells can be established using various methods including cell hashing 37 , the expressions of pre-defined barcodes 38 , or a combination of random barcodes 39 .”
Testing the plasticity-first theory of tumor evolution with spatiotemporal lineage tracingPreprint
Yang, J., Hou, L., Wang, X., Zhang, N., Bian, Y., Lu, Z., Chen, Y., Xie, D., Fang, Y., Wang, K., et al. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.08.06.668639 · Cited by 4
Boezio, G. L. M., Depotter, J. R. L., Frith, T. J. R., Radley, A., Strohbuecker, S., Cunha, A. C., Howell, M. T., & Briscoe, J. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.10.24.684328 · Cited by 5
DNMT3A regulates murine megakaryocyte-biased hematopoietic stem cell fate decisions
Waldvogel, S. M., Camacho, V., Fan, D., Guzman, A. G., Martell, A. G., Khabusheva, E., Pridgen, J. R., Fuente, J. D. L., Rau, R. E., Laidman, A. G., et al. (2025) · Blood Advances · doi:10.1182/bloodadvances.2024015061 · Cited by 3
GNODEVAE: a graph-based ODE-VAE enhances clustering for single-cell data
Fu, Z., Chen, C., Wang, S., Wang, J., & Chen, S. (2025) · BMC Genomics · doi:10.1186/s12864-025-11946-7 · Cited by 3
“Single-cell sequencing technologies capture molecular snapshots of cells that exist along continuous biological trajectories, whether during normal development, disease progression, or chromatin accessibility changes [52].”
Single-cell morphodynamics predict cell fate decisions during mucociliary epithelial differentiation
Tolonen, M., Xu, Z., Beker, O., Kapoor, V., Dumitrascu, B., & Sedzinski, J. (2026) · Molecular Systems Biology · doi:10.1038/s44320-026-00212-x · Cited by 5
“Furthermore, with the development of computational methods such as pseudotime and trajectory inference, it has become possible to reconstruct the temporal progression of these transitions from static transcriptomic snapshots (Trapnell et al, 2014; Weinreb et al, 2020; Weiler et al, 2024).”
Clonal tracing of blood stem cells across mouse and human lifespans
Rodriguez-Fraticelli, A. (2025) · Blood · doi:10.1182/blood.2024028195 · Cited by 4
Self-citationDeciphering splicing heterogeneity at single-cell resolution by SCSES
Xiao, W., Lv, X., Guo, D. H., Han, N., Zhou, L., Wang, P., & Liu, Z. (2025) · Nature Communications · doi:10.1038/s41467-025-64517-5 · Cited by 6
Maxwell, E. L., Taylor, I., Flegg, C., Liu, Y., Ranpura, G. N., Nooru‐Mohamed, F., Green, E. K., Huang, S., Guo, J., Teakle, N., et al. (2026) · Journal of Leukocyte Biology · doi:10.1093/jleuko/qiag004 · Cited by 2
Animals have expanded the evolutionary legacy of unicellular ancestors in blood cells
Nagahata, Y., Nishimura, Y., Kaitani, R., Leong, J. C. K., Oda‐Ishii, I., Kohtsuka, H., Abe, S., Ishida, T., Carmona-Rivas, M., Najle, S. R., et al. (2026) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2528110123 · Cited by 3
“Next, to rule out convergent evolution—where similar lifestyles might independently drive similar gene expression—we focused on transcription factors (TFs) whose expression profiles also identify cell lineages (18).”
Evolving strategies for lineage tracing: Genetic markers, synthetic barcodes, and natural variants
Kang, Z., Chen, H., Li, S., Wang, S., & Zhou, B. (2026) · Cell stem cell · doi:10.1016/j.stem.2026.05.001 · Cited by 6
Lineage tracing from cellular heritage to disease destiny
Zhu, H., Liu, X., Tang, M., Lui, K. O., & Zhou, B. (2026) · Nature Genetics · doi:10.1038/s41588-026-02628-5 · Cited by 5
“Systems such as LARRY, which links expressed barcodes to single-cell transcriptomics, have revealed that lineage potential is imprinted early through subtle transcriptional biases—effectively encoding a cell’s future fate in its past molecular state 47 .”
InfluentialExploring stem cell frontiers: definitions, challenges, and perspectives for regenerative medicine
Dardano, M., Lebek, T., & Tsang, I. H. C. (2024) · Biology Open · doi:10.1242/bio.060245 · Cited by 3
“Allon Klein (Harvard Medical School, MA, USA) contributed to the discourse about the stem cell concept with insights into cell types and states based on LARRY, a novel DNA-barcoding strategy for analysing clonal cell fates using single-cell RNA sequencing (Weinreb et al., 2020).”
BackgroundMaulding, N. D., Zou, J., Zhou, W., Metcalfe, C., Stuart, J. M., Ye, X., & Hafner, M. A. (2025) · npj Systems Biology and Applications · doi:10.1038/s41540-024-00485-8 · Cited by 3
Thomalla, J. M., & Wolfner, M. F. (2025) · Current topics in developmental biology/Current Topics in Developmental Biology · doi:10.1016/bs.ctdb.2025.01.001 · Cited by 3
Brain organoids and genome editing: A new era in understanding human brain development and disorders
Zhou, M., Cao, Y., Yue, K., Wu, W., Xie, Y., Hu, D., Zhao, J., Xu, F., Guo, J., Li, Z., et al. (2025) · Neural Regeneration Research · doi:10.4103/nrr.nrr-d-24-01546 · Cited by 3
“Weinreb et al. (2020) developed a LARRY (Lineage And RNA RecoverY) system to deliver DNA barcodes by lentiviral delivery.”
Decoding cell fate: integrated experimental and computational analysis at the single-cell level
Zhou, Y., Hou, S., Miao, X., Zhang, G., Li, Z., Zhang, D., Lin, Y., & Lin, Y. (2025) · Bioinformatics · doi:10.1093/bioinformatics/btaf603 · Cited by 4
“Cre-Lox lineage tracing is well-suited for in vivo studies, where Cre recombinase acts on polylox cassettes to excise or invert DNA fragments between loxP sites, generating a large (Weinreb et al. 2020) and polylox barcodes based on the Cre-Lox system (Pei et al. 2017). sgRNA, single-guide RNA.”
InfluentialMultistageOT: Multistage optimal transport infers trajectories from a snapshot of single-cell data
Tronstad, M., Karlsson, J., & Dahlin, J. S. (2025) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2516046122 · Cited by 5
Integrating natural and engineered genetic variations to decode regulatory influence on blood traits
Tardáguila, M., Schiller, D. V., Colombo, M., Gori, I., Coomber, E. L., Vanderstichele, T., Benaglio, P., Chiereghin, C., Gerety, S. S., Vuckovic, D., et al. (2026) · Cell Reports · doi:10.1016/j.celrep.2026.116937 · Cited by 3
Gutierrez, C., Vilas, C. K., Wu, C. J., & Al’Khafaji, A. M. (2022) · Frontiers in Immunology · doi:10.3389/fimmu.2022.859032 · Cited by 3
MLBKFD: Probabilistic Model Methods to Infer Pseudo Trajectories from Single-cell Data
Han, C., Cao, W., Li, C., Guo, Y., Wang, Y., Shi, Y.-Z., & Zhang, B. (2023) · Journal of Computational Biophysics and Chemistry · doi:10.1142/s2737416524400015 · Cited by 3
Computational single-cell methods for predicting cancer risk
Teschendorff, A. E. (2024) · Biochemical Society Transactions · doi:10.1042/bst20231488 · Cited by 3
Shaban, D., Najm, N., Droin, L., & Nijnik, A. (2024) · Stem Cell Reviews and Reports · doi:10.1007/s12015-024-10782-8 · Cited by 3
“…cells that correlate with specific cell fates, however, could not predict the differentiation trajectories based solely on the cellular transcriptome, indicating that epigenetic infor - mation rather than gene expression alone controls the differ - entiation programs of hematopoiesis [21 ].”
Arekatla, G., Skylaki, S., Suarez, D. C., Jackson, H. W., Schapiro, D., Engler, S., Auler, M. M. K., Ortega, G. C., Hastreiter, S., Reimann, A., et al. (2024) · Nature Communications · doi:10.1038/s41467-024-52069-z · Cited by 3
“This is addressed by recent studies combining cell tracking with end point single cell sequencing 20 – 23 , which however do not provide information about protein expression, subcellular localization, and posttranslational protein modi fi cations important for their regulation.”
Non-genetic differences underlie variability in proliferation among esophageal epithelial clones
Hueros, R. A. R., Gier, R. A., & Shaffer, S. M. (2024) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1012360 · Cited by 3
Computing hematopoiesis plasticity in response to genetic mutations and environmental stimulations
Wen, Y., He, H., Ma, Y., Bao, D., Cai, L. C., Wang, H., Li, Y., Zhao, B., & Cai, Z. (2024) · Life Science Alliance · doi:10.26508/lsa.202402971 · Cited by 3
“…approach is to use single-cell technology and combine with lineage tracking technologies (with implementing barcodes in stem cells or tracking mitochondrial mutation naturally); this approach may provide direct evidence about the trajectory of cells but technically challenging (56, 65, 66, 67).”
Natural Killer Cell–Mediated Cytotoxicity Shapes the Clonal Evolution of B-cell Leukemia
Buri, M. C., Shoeb, M. R., Bykov, A., Repiščák, P., Baik, H., Dupanovic, A., David, F. O., Kovačic, B., Hall‐Glenn, F., Dopa, S., et al. (2024) · Cancer Immunology Research · doi:10.1158/2326-6066.cir-24-0189 · Cited by 3
“It would certainly be interesting to delve deeper into secondary resistance mechanisms and to compare the transcriptomic profiles of primary and secondary resistant tumour cell clones in the future (59,60).”
Inferring Cell Differentiation Maps from Lineage Tracing Data
Sashittal, P., Zhang, R. Y., Law, B. K., Schmidt, H., Strzalkowski, A. J., Bolondi, A., Chan, M. M., & Raphael, B. J. (2025) · Lecture notes in computer science · doi:10.1007/978-3-031-90252-9_29 · Cited by 3
Cellular proliferation biases clonal lineage tracing and trajectory inference
Bonham-Carter, B., & Schiebinger, G. R. (2024) · Bioinformatics · doi:10.1093/bioinformatics/btae483 · Cited by 2
“…Zhang et al. 2021) and, inter-connected and in parallel, methods for inferring cell lineage from DNA barcodes (Biddy et al. 2018, Kester and van Oudenaarden 2018, Raj et al. 2018, Baron and van Oudenaarden 2019, McKenna and Gagnon 2019, Bowling et al. 2020, Sakata et al. 2020, Weinreb et al. 2020).”
BackgroundMethodconstclust: Consistent Clusters for scRNA-seqPreprint
Virshup, I., Choi, J., Cao, K. L., & Wells, C. A. (2020) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2020.12.08.417105 · Cited by 2
“When macrophages can be derived from different lineages, which hierarchies should they sit in [3]?”
BackgroundBest practices in designing, sequencing and identifying random DNA barcodesPreprint
Johnson, M. S., Venkataram, S., & Kryazhimskiy, S. (2022) · doi:10.32942/osf.io/t58xw · Cited by 3
Wiencke, J., Nissen, E. D., Koestler, D., Tamaki, S., Tamaki, C., Hansen, H. M., Warrier, G., Hadad, S., Mccoy, L. S., Rice, T., et al. (2023) · Research Square · doi:10.21203/rs.3.rs-3793353/v1 · Cited by 3
Deep Lineage: Single-cell lineage tracing and fate inference using deep learning
Sadria, M., Zhang, A. A., Lai, H. G., & Bader, G. D. (2026) · Cell Systems · doi:10.1101/2024.04.25.591126 · Cited by 4
“We tested this ability of Deep Lineage by using both fibroblast reprogramming and mouse hematopoiesis data (18,19) and performed a series of in silico perturbations to manipulate the fate of these systems.”
MethodSingh, I., Fernández-Pérez, D., Sanchez-Sanchez, P., & Rodriguez-Fraticelli, A. (2024) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.2139/ssrn.4831225 · Cited by 4
“…et al. 2019; Che et al. 2022), mouse models carrying different Cre/Flp-inducible leukemia mutations (Loberg et al. 2019), a new palette of LARRY expressed barcode libraries to track clones (Weinreb et al. 2020), and a sister-cell clone splitting strategy (Weinreb et al. 2020; Tian et al. 2021).”
BackgroundMethodInfluentialSelf-citationFinding signatures of low-dimensional geometric landscapes in high-dimensional cell fate transitionsPreprint
Yampolskaya, M., Ikonomou, L., & Mehta, P. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.05.27.656406 · Cited by 4
The hidden predictors of human haematopoietic clonal fatePreprint
Tomei, S., Zhang, S., Lin, M. C., Audiger, C., Weber, T., & Naik, S. H. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.07.28.667115 · Cited by 3
“In haematopoiesis, clonal multi-omics has largely been applied in murine studies in vivo 16 or in vitro 18,22,24 , but not yet for human haematopoiesis.”
Inferring Cell Differentiation Dynamics with Unobserved ProgenitorsPreprint
Howard-Snyder, W., Zhang, R., Schmidt, H., Chan, M. M., & Raphael, B. J. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2025.12.09.693214 · Cited by 3
“Instead, methods cluster cell types based on pair-wise distances defined over cell lineage trees [7, 16–18], use cell lineage trees to inform cellular trajectories [19], or optimize parsimony-like criteria [20].”
Liu, D., Song, B., Li, Z., Zhang, S., Fabiha, T., Zhao, J., Inoki, A., Piccand, J., Soh, C.-L., Dixon, G., et al. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2025.12.23.696311 · Cited by 4
Guo, W., Chen, Z., Li, X., et al. (2025) · Frontiers of Computer Science · doi:10.1007/s11704-025-41249-9 · Cited by 2
Lin, Y., Chen, X., Wu, L., Zhou, Y., & Lin, Y. (2026) · Cell Systems · doi:10.1016/j.cels.2026.101621 · Cited by 3
Pabba, M. K., Kuba, M., Kraus, T., Celikay, K., Meyer, J., Pradhan, S. K., Maiser, A., Harz, H., Leonhardt, H., Rohr, K., et al. (2025) · Advanced Science · doi:10.1002/advs.202505955 · Cited by 2
Shin, H. J., Sharma, S., Kronheim, S., et al. (2025) · STAR Protocols · doi:10.1016/j.xpro.2025.104229 · Cited by 2
Learning cell dynamics with neural differential equations
Vinyard, M. E., Rasmussen, A. W., Li, R., Klein, A. M., Getz, G. A., & Pinello, L. (2025) · Nature Machine Intelligence · doi:10.1038/s42256-025-01150-3 · Cited by 23
“26 and 27).”
InfluentialSelf-citationIntegrating synthetic biology to understand and engineer the heart, lung, blood, and sleep systems
Chaikof, E. L., Chen, J., Gillette, M. U., Boyer, L. A., Deans, T. L., Li, P., Hilton, I. B., Daniels, K. G., Goyal, Y., Mei, Y., et al. (2025) · Cell Systems · doi:10.1016/j.cels.2025.101446 · Cited by 2
CellRank: consistent and data view agnostic fate mapping for single-cell genomics
Weiler, P., & Theis, F. J. (2026) · Nature Protocols · doi:10.1038/s41596-025-01314-w · Cited by 3
“• Time-resolved murine bone marrow 44 : to speed up the corresponding procedure, we have subsampled the original data to 25% of the cells.”
Metabolic control of innate immune activation in TET2-mutant clonal hematopoiesis
Kim, P. G., Hergott, C. A., Miller, A. P., Deik, A., Boileau, M., Bullock, K. H., Pierce, K. A., Choy, A. H., Shin, W., McConkey, M. E., et al. (2026) · Cell chemical biology · doi:10.1016/j.chembiol.2026.01.006 · Cited by 3
Liu, Z., Jiang, S., Hu, T., Xue, Z., Chen, Y., Ruan, D., Xia, H., Feng, Y., Hao, J., Li, W., et al. (2026) · Patterns · doi:10.1016/j.patter.2025.101471 · Cited by 4
“We further evaluated the transferability of DestinyNet using the dataset from Weinreb et al. to assess its performance across experimental settings from in vitro to in vivo .”
InfluentialComputational approaches for multimodal lineage tracing
Wang, K., He, X., & Hu, Z. (2026) · Nature Reviews Genetics · doi:10.1038/s41576-026-00969-9 · Cited by 7
“…56–62 , reveal how progenitor cells diversify into multiple lineages 63–67 , identify early fate biases long before they manifest transcriptionally 46,68 , decode lineage plasticity in tumour evolution 48,49,69 and uncover gene regulatory programmes that control cell identity and plasticity…”
Comprehensive lineage tracing maps the landscape of cell fate decisions in mouse embryogenesis
Colgan, W. N., Koblan, L. W., Villagrana, J., Hou, T.-C. J., Wang, M., Gowri, G., Chandler, W., Sepúlveda, L. A., Ciftci, D., Smolyar, K., et al. (2026) · Cell · doi:10.1016/j.cell.2026.09.050 · Cited by 10
Progenitor diversity defines monocyte roles
Goodridge, H. S. (2023) · Blood · doi:10.1182/blood.2023021298 · Cited by 2
Same name, different game?—How ontogeny shapes classical monocyte phenotypes
Pimenov, L., Gonzalez, A. L., Doran, A. C., & Knapp, S. (2024) · Genes and Immunity · doi:10.1038/s41435-023-00248-1 · Cited by 2
“Therefore, the authors could validate that GMP give rise to CD177 + NeuMo, while CD319 + DCMo are progeny of MDP, supporting the model of binary monocyte ontogeny [2, 3].”
BackgroundResultSingle Cell Transcriptome Analysis During Development in Dictyostelium
Antolović, V., & Chubb, J. R. (2024) · Methods in molecular biology · doi:10.1007/978-1-0716-3894-1_16 · Cited by 2
Zhou, W., Lin, J., Hay, D. C., Yao, X., & Ouyang, H. (2024) · Medical Review · doi:10.1515/mr-2024-0056 · Cited by 3
Single-Cell Lineage Tracing and Clonal State-Fate Analysis
Rodriguez-Fraticelli, A., & Sánchez-Sánchez, P. (2025) · Methods in molecular biology · doi:10.1007/978-1-0716-4310-5_4 · Cited by 2
Self-citation
Metadata
- Authors
- Weinreb, C., Rodriguez-Fraticelli, A., Camargo, F. D., & Klein, A. M.
- Affiliations
- Harvard University (US)
- Boston Children's Hospital (US)
- Corresponding author
- Klein, Allon M.
- Venue
- Science
- Published
- 2020-02-14
- Volume
- 367
- Issue
- 6479
- ISSN
- 0036-8075 (ISSN-L)
- 1095-9203
- PMC
- PMC7608074
- OpenAlex
- W3002219094
- Semantic Scholar
- 572846affc0d995127aa26185ad9dadb30744381
- Cited by
- 759
- References
- 36
- Citations by year
- 2026: 166 · 2025: 121 · 2024: 110 · 2023: 92 · 2022: 90 · 2021: 101 · 2020: 71
- FWCI
- 25.30
- Type
- journal article
- Language
- en
- Open access
- green
- doi.org
- Licence
- cc-by-nc
- Locations
- Science (publishedVersion)
- bioRxiv (Cold Spring Harbor Laboratory) (submittedVersion, cc-by-nc)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- Funders
- National Institutes of Health (K99HL146983)
- NIH Office of the Director (R33CA212697-01, 1R01HL14102-01, HL128850-01A1, P01HL13147)
- American Society of Hematology (3391-19)
- Chan Zuckerberg Initiative (2018-182714)
- Harvard Stem Cell Institute (DP-0174-18-00)
- EMBO (ALTF 675-2015)
- 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 › Developmental Biology and Gene Regulation
- Keywords
- lineage tracing, clonal tracing, single-cell transcriptomics, hematopoiesis, cell fate determination, transcriptional landscape, hematopoietic hierarchy, granulopoiesis, monocyte differentiation, developmental hierarchy, stem cell differentiation
- MeSH terms
- RNA-Seq, Algorithms, Animals, Hematopoiesis, Monocytes, Gene Expression, Cell Lineage, Mice, DNA Barcoding, Taxonomic, Single-Cell Analysis, Transcriptome
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