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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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
“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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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
“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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- 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. 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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- Load-bearing: passes — The paper’s conclusions depend on it.
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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
Finding the missing heritability of complex diseases
Manolio, T. A., Collins, F. S., Cox, N. J., Goldstein, D. B., Hindorff, L. A., Hunter, D. J., McCarthy, M. I., Ramos, E. M., Cardon, L. R., Chakravarti, A., et al. (2009) · Nature · doi:10.1038/nature08494 · Cited by 8,336 · cited 1×
“Missing information of this type is akin to the notion of “missing heritability” in population genetics 29 .”
BackgroundTrapnell, C., Cacchiarelli, D., Grimsby, J., Pokharel, P., Li, S., Morse, M., Lennon, N. J., Livak, K. J., Mikkelsen, T. S., & Rinn, J. L. (2014) · Nature Biotechnology · doi:10.1038/nbt.2859 · Cited by 7,314 · cited 1×
“A common goal of scSeq is to order gene expression along dynamic trajectories by defining a “pseudotime” coordinate that orders transcriptomes (24).”
BackgroundLa Manno, G., Soldatov, R., Zeisel, A., Braun, E., Hochgerner, H., Petukhov, V., Lidschreiber, K., Kastriti, M. E., Lönnerberg, P., Furlan, A., et al. (2018) · Nature · doi:10.1038/s41586-018-0414-6 · Cited by 4,118
Droplet barcoding for single-cell transcriptomics applied to embryonic stem cells
Klein, A. M., Mazutis, L., Akartuna, I., Tallapragada, N., Veres, A., Li, V., Peshkin, L., Weitz, D. A., & Kirschner, M. W. (2015) · Cell · doi:10.1016/j.cell.2015.04.044 · Cited by 3,356
Self-citationA clonogenic common myeloid progenitor that gives rise to all myeloid lineages
Akashi, K., Traver, D., Miyamoto, T., & Weissman, I. L. (2000) · Nature · doi:10.1038/35004599 · Cited by 2,617 · cited 1×
“Cell culture and transplantation studies over several decades have led to the prevailing model of hematopoiesis as a branching hierarchy with defined faterestricted intermediates (8).”
BackgroundMi, H., Muruganujan, A., & Thomas, P. D. (2012) · Nucleic Acids Research · doi:10.1093/nar/gks1118 · Cited by 1,739
A revised airway epithelial hierarchy includes CFTR-expressing ionocytes
Montoro, D. T., Haber, A. L., Biton, M., Vinarsky, V., Lin, B., Birket, S. E., Yuan, F., Chen, S., Leung, H. M., Villoria, J., et al. (2018) · Nature · doi:10.1038/s41586-018-0393-7 · Cited by 1,206 · cited 1×
“This approach can map the fate of cells from a continuous landscape of starting states and does not require isolation or labeling of specific prospective progenitor populations (2, 14).”
BackgroundA single-cell atlas of the airway epithelium reveals the CFTR-rich pulmonary ionocyte
Plasschaert, L. W., Žilionis, R., Choo-Wing, R., Savova, V., Knehr, J., Roma, G., Klein, A. M., & Jaffe, A. B. (2018) · Nature · doi:10.1038/s41586-018-0394-6 · Cited by 1,047
Self-citationHuman haematopoietic stem cell lineage commitment is a continuous process
Velten, L., Haas, S. F., Raffel, S., Blaszkiewicz, S., Islam, S., Hennig, B. P., Hirche, C., Lutz, C., Buss, E. C., Nowak, D., et al. (2017) · Nature Cell Biology · doi:10.1038/ncb3493 · Cited by 925 · cited 1×
“Consistent with recent scSeq studies (19), progenitors with different fate potentials did not partition into discrete cell states, but instead formed a structured continuum.”
BackgroundSingle-cell mapping of gene expression landscapes and lineage in the zebrafish embryo
Wagner, D. E., Weinreb, C., Collins, Z. M., Briggs, J. A., Megason, S. G., & Klein, A. M. (2018) · Science · doi:10.1126/science.aar4362 · Cited by 849 · cited 1×
“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.”
BackgroundSelf-citation
Cited by
Sorted by citations
Items 301–400 of 723
Huang, X., Jannu, A. J., Song, Z., Jury, N., Lasagna‐Reeves, C. A., Initiative, A. D. N., Johnson, T. S., Huang, K., & Zhang, J. (2025) · Alzheimer s & Dementia · doi:10.1002/alz.14241 · Cited by 14
“23 We compared our OT-based label transfer method with the state-of-the-art methods to evaluate the performance against them.”
Toward the dissection of hematopoietic stem cell fates and their determinants
Shang, F., & Rodewald, H.-R. (2022) · Current Opinion in Genetics & Development · doi:10.1016/j.gde.2022.101945 · Cited by 11
Watt, S. M. (2022) · PubMed · doi:10.12336/biomatertransl.2022.01.005 · Cited by 11
Single-Cell Molecular Barcoding to Decode Multimodal Information Defining Cell States
Kim, I. S. (2023) · Molecules and Cells · doi:10.14348/molcells.2023.2168 · Cited by 11
“This approach helps to elucidate when a cell decides its fate in development and which cancer cells survive after drug treatment (Eyler et al., 2020; Kim et al., 2020; Weinreb et al., 2020).”
Predicting anti-cancer drug combination responses with a temporal cell state network model
Sarmah, D., Meredith, W. O., Weber, I. K., Price, M. R., & Birtwistle, M. R. (2023) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1011082 · Cited by 10
Li, Y., Chen, S., Liu, W., Zhao, D., Gao, Y., Hu, S., Liu, H., Li, Y., Qu, L., & Liu, X. (2024) · Nature Communications · doi:10.1038/s41467-023-42677-6 · Cited by 12
“Forexample,scRNA-seqanalysesof cultured cells of the mouse hematopoietic system detected more than a dozen of differentially expressed genes between mature monocytes derived from granulocyte-monocyte progenitors and those from monocyte-dendritic progenitors 64 .”
Applications of high-resolution clone tracking technologies in cancer
Morgan, D., Jost, T. A., Santiago, C. D., & Brock, A. (2021) · Current Opinion in Biomedical Engineering · doi:10.1016/j.cobme.2021.100317 · Cited by 11
Deciphering cell-fate trajectories using spatiotemporal single-cell transcriptomic data
Zhang, Z., Wang, Z., Sun, Y., Shen, J., Peng, Q., Li, T., & Zhou, P. (2025) · npj Systems Biology and Applications · doi:10.1038/s41540-025-00624-9 · Cited by 16
“Anotherpromising directionistheincorporationoflineagetracingandclonalrecording technologies 163 – 168 , which will allow computationally inferred trajectories to be validated and re fi ned with experimentally observed ancestry, thus strengthening the biological interpretation of fate decisions.”
Expressed Barcoding Enables High-Resolution Tracking of the Evolution of Drug Tolerance
Cotton, J. L., Estrada, J., Sagar, V., Chen, J. T., Piquet, M., Alford, J., Song, Y., Li, X., Riester, M., DiMare, M. T., et al. (2023) · Cancer Research · doi:10.1158/0008-5472.can-23-0144 · Cited by 12
Unraveling the secrets of γδ T cells with single-cell biology
Sagar (2023) · Journal of Leukocyte Biology · doi:10.1093/jleuko/qiad131 · Cited by 10
Label-free metabolic optical biomarkers track stem cell fate transition in real time
Zhou, H., Li, I. A., Bramlett, C. S., Wang, B., Hao, J., Yen, D. P., Ando, Y., Fraser, S. E., Lu, R., & Shen, K. (2024) · Science Advances · doi:10.1126/sciadv.adi6770 · Cited by 11
“Single-cell RNA sequencing data for freshly isolated (GSE81682) and in vitro – cultured (GSM4185642) HSPCs were retrieved from previous publications ( 5 , 49 ).”
BackgroundMethodMultiscale engineering of brain organoids for disease modeling
Xu, C., Alameri, A., Leong, W., Johnson, E., Chen, Z., Xu, B., & Leong, K. W. (2024) · Advanced Drug Delivery Reviews · doi:10.1016/j.addr.2024.115344 · Cited by 12
EnhancerNet: a predictive model of cell identity dynamics through enhancer selection
Karin, O. (2024) · Development · doi:10.1242/dev.202997 · Cited by 10
Fiorenza, S., Zheng, Y., Purushe, J., Bock, T., Sarthy, J. F., Janssens, D. H., Sheih, A., Kimble, E. L., Kirchmeier, D., Phi, T.-D., et al. (2024) · Nature Communications · doi:10.1038/s41467-024-52503-2 · Cited by 10
A systems view of cellular heterogeneity: Unlocking the “wheel of fate”
Movasat, H., Giacopino, E., Shahdoost, A., Nokoorani, Y. D., Abrbekouh, A. H., Tahamtani, Y., & Shakiba, N. (2025) · Cell Systems · doi:10.1016/j.cels.2025.101300 · Cited by 15
Recent advances in single‐cell multimodal analysis to study immune cells
Louie, R. H. Y., & Luciani, F. (2020) · Immunology and Cell Biology · doi:10.1111/imcb.12432 · Cited by 10
“This approach has been applied to HSC using a lentiviral delivery system.(38) Cells cultured in vitro and cells transplanted in vivo were collected over several days, and then sorted to isolate oligopotent and multipotent progenitor cells using flow cytometric markers.”
MethodIdentify differential genes and cell subclusters from time-series scRNA-seq data using scTITANS
Shao, L., Xue, R., Lu, X., Liao, J., Shao, X., & Fan, X. (2021) · Computational and Structural Biotechnology Journal · doi:10.1016/j.csbj.2021.07.016 · Cited by 11
“Based on a snapshot of the transcriptome of thousands of single cells in a cell population, scRNA-seq has proven powerful in detecting heterogeneity among individual cells and delineating cell maps [4-6].”
Computational tools for analyzing single-cell data in pluripotent cell differentiation studies
Ding, J., Alavi, A., Ebrahimkhani, M. R., & Bar‐Joseph, Z. (2021) · Cell Reports Methods · doi:10.1016/j.crmeth.2021.100087 · Cited by 10
“Some of the more widely used include Monocole3 (Cao et al., 2019), Diffusion maps (Haghverdi et al., 2016), and PAGA (Wolf et al., 2019) (Table 1).”
MethodOncogenes and the Origins of Leukemias
Brown, G. (2022) · International Journal of Molecular Sciences · doi:10.3390/ijms23042293 · Cited by 15
“Analysis of the transcriptomes of the cells that were reovered p st-transplant tion also revealed a continuum landscape for hematopoiesis from multip tent cells with pathways, as to near-neighbors, toward e ythrocytes, basophils, neutrophils, monocytes, dend itic cells, B cells, and T cells [17].”
BackgroundWollenzien, H., Tecleab, Y. A., Sloane, R. A. S., Restaino, A. C., & Kareta, M. S. (2023) · Molecular Cancer Research · doi:10.1158/1541-7786.mcr-22-0881 · Cited by 10
In-vivo differentiation of adult hematopoietic stem cells from a single-cell point of view
Nazaraliyev, A., Richard, É., & Sawai, C. M. (2020) · Current Opinion in Hematology · doi:10.1097/moh.0000000000000587 · Cited by 11
Genetic association and characterization ofFSTL5in isolated clubfoot
Khanshour, A. M., Kidane, Y. H., Kozlitina, J., et al. (2020) · Human Molecular Genetics · doi:10.1093/hmg/ddaa236 · Cited by 10
“As the transcriptional landscape of lineage subclusters may help to broadly inform future cell fates (25), we extracted all genes with significantly enriched expression within Fstl5-expressing subclusters and performed pathway analyses.”
MethodVariability within rare cell states enables multiple paths towards drug resistancePreprint
Emert, B. L., Cote, C. J., Torre, E. A., et al. (2020) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2020.03.18.996660 · Cited by 10
Lineage Recording Reveals the Phylodynamics, Plasticity and Paths of Tumor EvolutionPreprint
Yang, D., Jones, M. G., Naranjo, S., Rideout, W. M., Min, K. H., Ho, R., Wu, W., Replogle, J. M., Page, J. L., Quinn, J. J., et al. (2021) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2021.10.12.464111 · Cited by 10
“To investigate the consistency of evolutionary paths across tumors, we developed a statistic termed “Evolutionary Coupling”, which extends a clonal coupling statistic (Wagner et al., 2018; Weinreb et al., 2020) to quantify the phylogenetic distance between pairs of cell states (STAR Methods).”
MethodAdvancements in prospective single-cell lineage barcoding and their applications in researchPreprint
Zhang, X., Huang, Y., Yang, Y., Wang, Q., & Li, L. (2024) · Genome Research · doi:10.1101/gr.278944.124 · Cited by 10
Interpretation, extrapolation and perturbation of single cells
Dimitrov, D., Schrod, S., Rohbeck, M., & Stegle, O. (2026) · Nature Reviews Genetics · doi:10.1038/s41576-025-00920-4 · Cited by 26
DNA Barcoding Technology for Lineage Recording and Tracing to Resolve Cell Fate Determination
Kim, I. S. (2023) · Cells · doi:10.3390/cells13010027 · Cited by 12
“During the differentiation process of mouse hematopoietic stem cells (HSCs) and progenitor cells, LARRY incorporated 28-mer random sequences into the UTR of GFP transgene and extracted subpopulations at different time points [25].”
BackgroundNovel insights into the ontogeny of basophils
Miyake, K., Ito, J., & Karasuyama, H. (2024) · Frontiers in Allergy · doi:10.3389/falgy.2024.1402841 · Cited by 9
“Weinreb et al. traced hematopoietic lineage differentiation by combining DNA barcoding with scRNA-seq technologies (55).”
FateNet: an integration of dynamical systems and deep learning for cell fate prediction
Sadria, M., & Bury, T. M. (2024) · Bioinformatics · doi:10.1093/bioinformatics/btae525 · Cited by 13
“To test FateNet on biological data, we use temporal scRNA-seq data of mouse hematopoietic stem cell differentiation with 130 887 cells and 25 289 genes (Weinreb et al. 2020).”
MethodDeciphering the effect of UM171 on human hematopoietic progenitor cell fate through clonal analysis
Coulombe, P., Tomellini, E., Chagraoui, J., Mayotte, N., & Sauvageau, G. (2025) · Nature Communications · doi:10.1038/s41467-024-55225-7 · Cited by 8
“…this cell-tagging approach strongly demonstrated that mast cells can be derived from a precursor common to the MK-Erythroid lineage, a correlation previously suggested based on in silico pseudotime or graph-approximation observations 19,20 and lineage tracing of mouse hematopoiesis 21 .”
Integrating Dynamical Systems Modeling with Spatiotemporal scRNA-Seq Data Analysis
Zhang, Z., Sun, Y., Peng, Q., Li, T., & Zhou, P. (2025) · Entropy · doi:10.3390/e27050453 · Cited by 18
Perfect duet: Dual recombinases improve genetic resolution
Li, H., Weng, W., & Zhou, B. (2023) · Cell Proliferation · doi:10.1111/cpr.13446 · Cited by 7
“Lineage tracing is the identification of all descendants of the marked founder cells, which provides information about the progeny status.(1)”
BackgroundDeciphering transcriptional and functional heterogeneity in hematopoiesis with single-cell genomics
Martin-Rufino, J. D., & Sankaran, V. G. (2021) · Current Opinion in Hematology · doi:10.1097/moh.0000000000000657 · Cited by 9
Defining the Emerging Blood System During Development at Single-Cell Resolution
Karlsson, G., Sommarin, M. N. E., & Böiers, C. (2021) · Frontiers in Cell and Developmental Biology · doi:10.3389/fcell.2021.660350 · Cited by 11
“The study highlights that lineage commitment and cell fate choice may occur at multiple levels and thus the benefit of combining chromatin accessibility with transcriptome analysis (Buenrostro et al., 2018; Weinreb et al., 2020).”
BackgroundInfluentialLineages of embryonic stem cells show non-Markovian state transitions
Udomlumleart, T., Hu, S., & Garg, S. (2021) · iScience · doi:10.1016/j.isci.2021.102879 · Cited by 9
“On the other hand, a recent study of hematopoiesis in vitro and in vivo captured both full transcriptomic information and lineage for single cells, yet found sister progenitors to have intrinsic fate biases that could not be accounted for by the transcriptome (Weinreb et al., 2020).”
BackgroundInduced cell-autonomous neutropenia systemically perturbs hematopoiesis in Cebpa enhancer-null mice
Avellino, R., Mulet‐Lazaro, R., Havermans, M., Hoogenboezem, R. M., Smeenk, L., Salomonis, N., Schneider, R. K., Rombouts, E. J. C., Bindels, E. M., Grimes, H. L., et al. (2021) · Blood Advances · doi:10.1182/bloodadvances.2021005851 · Cited by 9
Transcriptomic heterogeneity of cultured ADSCs corresponds to embolic risk in the host
Yan, K., Zhang, J., Yin, W., Harding, J. N., Ma, F., Wu, D., Deng, H., Han, P., Li, R., Peng, H., et al. (2022) · iScience · doi:10.1016/j.isci.2022.104822 · Cited by 10
Zhang, Q., Olofzon, R., Konturek-Cieśla, A., Yuan, O., & Bryder, D. (2023) · eLife · doi:10.7554/elife.91826 · Cited by 16
Hematopoietic Stem Cells in Health and Disease—Insights from Single-Cell Multi-omic Approaches
Haas, S. F. (2020) · Current Stem Cell Reports · doi:10.1007/s40778-020-00174-2 · Cited by 8
Bordenave, J., Gajda, D., Michonneau, D., Vallet, N., Chevalier, M. F., Clappier, E., Lemaire, P., Mathis, S., Robin, M., Xhaard, A., et al. (2024) · Journal of Clinical Investigation · doi:10.1172/jci180331 · Cited by 9
Dual-nuclease single-cell lineage tracing by Cas9 and Cas12a
Chen, C., Liao, Y., Zhu, M., Wang, L., Yu, X., Li, M., & Peng, G. (2024) · Cell Reports · doi:10.1016/j.celrep.2024.115105 · Cited by 11
Revealing a coherent cell-state landscape across single-cell datasets with CONCORD
Zhu, Q., Jiang, Z., Zuckerman, B., Weinberger, L. S., Thomson, M. W., & Gartner, Z. J. (2026) · Nature Biotechnology · doi:10.1038/s41587-025-02950-z · Cited by 14
Charting single-cell lineages with synthetic and natural barcodes
Rodriguez-Fraticelli, A., & Parreno, V. (2026) · Nature Reviews Genetics · doi:10.1038/s41576-026-00943-5 · Cited by 11
“With the advent of single-cell transcriptome sequencing 293 , the use of expressed barcodes enabled simultaneous capture of cell identity and lineage, breaking free from marker-based cell isolation and allowing the characterization of specific determinants of lineage decisions 23,28,29,32–34 .”
InfluentialSelf-citationTowards a New Understanding of Decision-Making by Hematopoietic Stem Cells
Brown, G. (2020) · International Journal of Molecular Sciences · doi:10.3390/ijms21072362 · Cited by 8
Integrating single-cell genomics pipelines to discover mechanisms of stem cell differentiation
Shen, S., Sun, Y., Matsumoto, M., Shim, W. J., Sinniah, E., Wilson, S. B., Werner, T., Wu, Z., Bradford, S. T., Hudson, J. E., et al. (2021) · Trends in Molecular Medicine · doi:10.1016/j.molmed.2021.09.006 · Cited by 8
CLONAL HEMATOPOIESIS: ROLE IN HEMATOLOGIC NON-HEMATOLOGIC
Testa, U., Castelli, G., & Pelosi, E. (2022) · Mediterranean Journal of Hematology and Infectious Diseases · doi:10.4084/mjhid.2022.069 · Cited by 9
“63% of clonal hematopoiesis cases with mCAs co-occurred with at least one gene mutation, particularly with high mutation number and VAF.(47) CHIP mutations were observed in 30% of these cancer patients.”
BackgroundMethodInfluentialWang, L., Nie, R., Zhang, J., & Cai, J. (2022) · BMC Bioinformatics · doi:10.1186/s12859-022-05098-8 · Cited by 7
“Lineage tracing can track cells across time, linking the cells from a less differentiated state to a more differentiated state [16, 17].”
BackgroundZhang, Q., Olofzon, R., Konturek-Cieśla, A., Yuan, O., & Bryder, D. (2024) · eLife · doi:10.7554/elife.91826.3 · Cited by 8
Macrophage imaging and subset analysis using single-cell RNA sequencing
Arlauckas, S. P., Oh, N., Li, R., Weissleder, R., & Miller, M. A. (2020) · Nanotheranostics · doi:10.7150/ntno.50185 · Cited by 8
“New methods to barcode monocytes for lineage tracing are currently in development [190].”
MethodDevelopmental stochasticity and variation in floral phyllotaxis
Kitazawa, M. S. (2021) · Journal of Plant Research · doi:10.1007/s10265-021-01283-7 · Cited by 7
“Examples from animals have shown that mechanical properties of cells and molecular fluctuations in the dynamics of signaling pathways affect cellular fate decision (Kroll et al. 2020; Weinreb et al. 2020; Zechner et al. 2020).”
BackgroundDefining cell identity beyond the premise of differential gene expression
Kim, H. J., Tam, P., & Yang, P. (2021) · Cell Regeneration · doi:10.1186/s13619-021-00083-7 · Cited by 8
Hoenow, S., Yan, I., Noll, J., Groneberg, M., Casar, C., Lory, N. C., Vogelsang, M., Hansen, C. S., Wolf, V., Fehling, H., et al. (2022) · Cells · doi:10.3390/cells11162539 · Cited by 6
“Under conditions of homeostasis three more proinflammatory monocyte subsets: nMO, dcMO [12,13,43,44], and Cxcl10+ and Saa3+ monocytes were identified [14].”
BackgroundCell types as species: Exploring a metaphor
Doyle, J. J. (2022) · Frontiers in Plant Science · doi:10.3389/fpls.2022.868565 · Cited by 10
“” What information should such models incorporate, beyond transcriptomic data? Could chromatin criteria be incorporated (Weinreb et al., 2020; Winick-Ng et al., 2021)?”
BackgroundAbed, L. H., Tak, T., Cosgrove, J., & Perié, L. (2022) · Frontiers in Medicine · doi:10.3389/fmed.2022.919345 · Cited by 7
“analysis of murine hematopoietic progenitor differentiation in vitro (35).”
BackgroundMethodInfluentialSingle Cell Transcriptomics to Understand HSC Heterogeneity and Its Evolution upon Aging
Hérault, L., Poplineau, M., Rémy, É., & Duprez, E. (2022) · Cells · doi:10.3390/cells11193125 · Cited by 9
“They additionally showed, using computational methods of dynamic inference that fate choice occurs earlier than detected by the algorithms and that cells progress smoothly in the differentiation with precise and consistent dynamics [105].”
BackgroundLearning cell identity in immunology, neuroscience, and cancer
Medina, S., Ihrie, R. A., & Irish, J. M. (2022) · Seminars in Immunopathology · doi:10.1007/s00281-022-00976-y · Cited by 9
“Lineage inference techniques including single cell RNA sequencing have also suggested new models of cellular development or markers identifying transitional states [49].”
BackgroundBehind the monocyte’s mystique: uncovering their developmental trajectories and fates
Teh, Y. C., Chooi, M. Y., & Chong, S. Z. (2023) · Discovery Immunology · doi:10.1093/discim/kyad008 · Cited by 12
“function [19, 20].”
BackgroundIntegrating lineage tracing and single-cell analysis
Tang, L. (2020) · Nature Methods · doi:10.1038/s41592-020-0802-3 · Cited by 8
Reading the heart at single-cell resolution
Zhou, B., & Wang, L. (2020) · Journal of Molecular and Cellular Cardiology · doi:10.1016/j.yjmcc.2020.08.010 · Cited by 8
Akimov, Y. A., Bulanova, D. R., Abyzova, M. S., Wennerberg, K., & Aittokallio, T. A. (2019) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/622506 · Cited by 7
Multiomic single-cell lineage tracing to dissect fate-specific gene regulatory programsPreprint
Jindal, K., Adil, M. T., Yamaguchi, N., Yang, X., Wang, H. C., Kamimoto, K., Rivera-Gonzalez, G. C., & Morris, S. A. (2022) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2022.10.23.512790 · Cited by 8
LoxCode in vivo barcoding resolves epiblast clonal fate to fetal organsPreprint
Weber, T. S., Biben, C., Miles, D. C., Glaser, S. P., Tomei, S., Lin, M. C., Kueh, A. J., Pal, M., Zhang, S., Tam, P., et al. (2023) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2023.01.02.522501 · Cited by 8
Metabolically Primed Multipotent Hematopoietic Progenitors Fuel Innate ImmunityPreprint
Cosgrove, J., Lyne, A.-M., Rodríguez‐Ramiro, I., Cabeli, V., Conrad, C., Bento, S. T., Tubeuf, E., Russo, E., Tabarin, F., Belloucif, Y., et al. (2023) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2023.01.24.525166 · Cited by 9
A multi-track landscape of haematopoiesis informed by cellular barcoding and agent-based modellingPreprint
Lin, D. S. Y., Zhang, S., Schreuder, J., Tran, J., Sargeant, T., Metcalf, D., Ng, A. P., Weber, T. S., & Naik, S. H. (2024) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2024.03.28.587126 · Cited by 8
“…SIS-seq (Luyi et al., 2018; Tian et al., 2021) or similar approaches (Biddy et al., 2018; Emert et al., 2021; Rodriguez-Fraticelli et al., 2020; Weinreb et al., 2020) provided powerful new insights, including the surprising result that genes that predict fate bias or other clonal properties…”
BackgroundMethodResultInfluentialInferring 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) · Nature Methods · doi:10.1101/2024.09.09.611835 · Cited by 21
“On lineage tracing data from a mouse hematopoiesis model [30], Carta infers a cell differentiation map that better recapitulates the established differentiation of hematopoiesis and also has stronger agreement with gene expression compared to existing methods.”
InfluentialSchlüter, H. M., & Uhler, C. (2025) · Nature Communications · doi:10.1038/s41467-025-56388-7 · Cited by 7
RNA splicing factor Rbm25 underlies heterogeneous preleukemic clonal expansion in mice
Bramlett, C. S., Eerdeng, J., Du, J., Lee, Y., Garcia, I., Vergel-Rodriguez, M., Condie, P., Nogalska, A., & Lu, R. (2023) · Blood · doi:10.1182/blood.2023019620 · Cited by 8
Systems biology approaches to unravel lymphocyte subsets and function
Kim, Y., Greenleaf, W. J., & Bendall, S. C. (2023) · Current Opinion in Immunology · doi:10.1016/j.coi.2023.102323 · Cited by 7
Clonal tracking in cancer and metastasis
Aalam, S. M. M., Nguyen, L. V., Ritting, M. L., & Kannan, N. (2023) · Cancer and Metastasis Reviews · doi:10.1007/s10555-023-10149-4 · Cited by 8
“This approach has been applied to understand the clonal development of the central nervous system, reprogrammed cells, hematopoiesis, and AML and zebrafish development studies [67, 72, 74, 117, 118, 120].”
Mircea, M., & Semrau, S. (2021) · Biochemical Society Transactions · doi:10.1042/bst20210135 · Cited by 9
Ye, J., Calvo, I. A., Cenzano, I., Vilas‐Zornoza, A., Martinez‐de‐Morentin, X., Lasaga, M., Alignani, D., Paiva, B., Viñado, A. C., Martín‐Úriz, P. S., et al. (2022) · iScience · doi:10.1016/j.isci.2022.104225 · Cited by 6
Clonal selection parallels between normal and cancer tissues
Salavaty, A., Azadian, E., Naik, S. H., & Currie, P. D. (2023) · Trends in Genetics · doi:10.1016/j.tig.2023.01.007 · Cited by 8
Quantifying cancer cell plasticity with gene regulatory networks and single-cell dynamics
Groves, S. M., & Quaranta, V. (2023) · Frontiers in Network Physiology · doi:10.3389/fnetp.2023.1225736 · Cited by 11
“Alternatively, lineage tracing methods have long been used to understand the temporal dynamics of cell state in cancer populations, particularly during tumor initiation and in response to treatment (Chakrabarti et al., 2018; Wagner et al., 2018; Weinreb et al., 2020;Wang et al., 2021; Singh and Saint-Antoine, 2023).”
MethodHussen, K. A., Chabaane, E., Nelson, E., Lekiashvili, S., Diop, S., Keita, S., Evrard, B., Lardenois, A., Delord, M., Verhoeyen, E., et al. (2023) · iScience · doi:10.1016/j.isci.2023.107890 · Cited by 7
“Despite this, there is still debate regarding when and how progenitors execute fate decisions and if lineage choices are primarily conditioned by intrinsic cell properties, stochastic behavior or dictated by extracellular signals.(12) Further, increasing evidence indicates that lymphoid and myeloid lineages are subjec…”
BackgroundCell fate decision in erythropoiesis: Insights from multiomics studies
Tur, S., Palii, C. G., & Brand, M. (2024) · Experimental Hematology · doi:10.1016/j.exphem.2024.104167 · Cited by 7
In vivo and ex vivo haematopoietic stem cell expansion
Yamamoto, R., Wilkinson, A. C., & Nakauchi, H. (2020) · Current Opinion in Hematology · doi:10.1097/moh.0000000000000593 · Cited by 7
“Application of novel technologies such as single cell RNA-seq [26] and lineage tracing technologies [27] will also be interesting to apply here, as well as live cell imaging technologies such as those recently used to track asymmetric cell division of mouse HSCs ex vivo [28].”
BackgroundSingle-cell lineage tracing approaches in hematology research: technical considerations
Carrelha, J., Lin, D. S. Y., Rodriguez-Fraticelli, A., Luis, T. C., Wilkinson, A. C., Cabezas‐Wallscheid, N., Tremblay, C. S., & Haas, S. F. (2020) · Experimental Hematology · doi:10.1016/j.exphem.2020.07.007 · Cited by 7
Self-citationRebuilding limbs, one cell at a time
Leigh, N. D., & Currie, J. D. (2022) · Developmental Dynamics · doi:10.1002/dvdy.463 · Cited by 6
“This would allow one to determine clone behaviors and lineage connections over repeated amputations which should, in principle, allow one to determine if clone behaviors are heritable or are largely the product of location and proximity to extrinsic cues for migration, proliferation, and so forth.(45,46,50,51) The lim…”
BackgroundTran, A., Yang, P., Yang, J. Y. H., & Ormerod, J. T. (2022) · Briefings in Functional Genomics · doi:10.1093/bfgp/elac008 · Cited by 7
“These methods have enabled us to observe the regulatory processes within cells at an unprecedented resolution, revealing the heterogeneity and stochasticity of cells [14, 15].”
BackgroundMethodWild, S. A., Cannell, I. G., Kania, K., Nicholls, A., Bressan, D., CRUK IMAXT Grand Challenge Team, Hannon, G. J., & Sawicka, K. (2022) · eLife · doi:10.7554/elife.80981 · Cited by 7
Gene expression noise dynamics unveil functional heterogeneity of ageing hematopoietic stem cellsPreprint
Rosales-Alvarez, R. E., Rettkowski, J., Herman, J. S., Dumbović, G., Cabezas‐Wallscheid, N., & Grün, D. (2022) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2022.08.04.502776 · Cited by 7
“For example, single-cell lineage tracing by random cellular barcoding revealed early lineage priming of hematopoietic stem cells (HSCs) (Weinreb et al., 2020), which remained undetected when relying on single-cell RNA-seq (scRNA-seq) data alone.”
BackgroundExtraction and quantification of lineage-tracing barcodes with NextClone and CloneDetectivePreprint
Putri, G., Pires, N., Davidson, N., Blyth, C., Al’Khafaji, A. M., Goel, S., & Phipson, B. (2023) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2023.11.19.567755 · Cited by 7
“Recent advances in lineage-tracing protocols, such as ClonMapper [1], SPLINTR [2], Larry [3] and LoxCode [4], which integrate DNA barcoding with single-cell RNA sequencing (scRNA-seq), have catalysed significant progress in clonal biology.”
BackgroundVinyard, M. E., Rasmussen, A. W., Li, R., Klein, A. M., Getz, G. A., & Pinello, L. (2023) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2023.12.06.570508 · Cited by 7
“Here, we adapt and build on benchmark tasks that have previously been used in conjunction with this LARRY dataset 12,24 to compare the accuracy of models aimed at learning and predicting cellular dynamics on two tasks: fate bias prediction (Task 1) and prediction of intermediate cell states at…”
BackgroundMethodSelf-citationPriam, P., Krasteva, V., Rousseau, P., Polsinelli, A., Côté, L., Desanlis, I., Farah, A., Lavallée, V., Kmita, M., & Lessard, J. A. (2024) · Developmental Cell · doi:10.1016/j.devcel.2024.08.007 · Cited by 6
Fitness and transcriptional plasticity of human breast cancer single-cell-derived clones
Nguyen, L. V., Eyal-Lubling, Y., Guerrero-Romero, D., Kronheim, S., Chin, S., García, R. M., Sammut, S., Lerda, G., Lui, A. J. W., Bardwell, H. A., et al. (2025) · Cell Reports · doi:10.1016/j.celrep.2025.115699 · Cited by 7
Toward a systems-level probing of tumor clonality
Grody, E. I., Abraham, A., Shukla, V., & Goyal, Y. (2023) · iScience · doi:10.1016/j.isci.2023.106574 · Cited by 5
A multi-kingdom genetic barcoding system for precise clone isolation
Ishiguro, S., Ishida, K., Sakata, R. C., Ichiraku, M., Takimoto, R., Yogo, R., Kijima, Y., Mori, H., Tanaka, M., King, S. B., et al. (2025) · Nature Biotechnology · doi:10.1038/s41587-025-02649-1 · Cited by 10
Ho, V. W., Grainger, D. E., Chagraoui, H., & Porcher, C. (2022) · Seminars in Cell and Developmental Biology · doi:10.1016/j.semcdb.2022.01.008 · Cited by 5
Single Cell Approaches to Understand the Earliest Steps in Heart Development
Lescroart, F., & Zaffran, S. (2022) · Current Cardiology Reports · doi:10.1007/s11886-022-01681-w · Cited by 8
“However, new tools have been recently developed to reconstruct lineage history from single cell sequencing, using CRISPR/Cas9 genomic editing or recombination for generation of lineage barcodes [40], [69].”
MethodA universal sequencing read interpreter
Kijima, Y., Evans-Yamamoto, D., Toyoshima, H., & Yachie, N. (2023) · Science Advances · doi:10.1126/sciadv.add2793 · Cited by 8
Lu, X., Lofgren, S., Zhao, Y., & Mazur, P. K. (2023) · Nature Biomedical Engineering · doi:10.1038/s41551-023-01085-3 · Cited by 6
Sadria, M., Layton, A. T., Goyal, S., & Bader, G. D. (2024) · Cell Reports Methods · doi:10.1016/j.crmeth.2024.100819 · Cited by 23
“23”
InfluentialEngineered multicellular niches for pluripotent stem cell–derived immunotherapy
Smerchansky, M. E., & Kinney, M. A. (2020) · Current Opinion in Biomedical Engineering · doi:10.1016/j.cobme.2020.06.002 · Cited by 6
Differentiation is accompanied by a progressive loss in transcriptional memory
Fourneaux, C., Racine, L., Koering, C., Dussurgey, S., Vallin, E., Moussy, A., Parmentier, R., Brunard, F., Stockholm, D., Modolo, L., et al. (2024) · BMC Biology · doi:10.1186/s12915-024-01846-9 · Cited by 6
“Another type of approach that can be used in this context consists of genetically tagging the cells in order to generate genetic barcodes which will be passed along progenies [27].”
BackgroundRatz, M., von Berlin, L., Larsson, L., Martin, M., Westholm, J. O., Manno, G. L., Lundeberg, J., & Frisén, J. (2021) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2021.08.31.458418 · Cited by 6
Pubertal sex hormones control transcriptional trajectories in the medial preoptic areaPreprint
Hashikawa, K., Hashikawa, Y., Liu, Y., Rossi, M. A., Basiri, M. L., Chen, J., Ahmad, O. R., Mukundan, R. V., Johnston, N. L., McHenry, J. A., et al. (2021) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2021.09.02.458782 · Cited by 6
“This assumption predicts that fate of single cells can be decoded solely from the gene expression in scRNAseq data (Weinreb et al., 2020).”
BackgroundMinkina, A., Cao, J., & Shendure, J. (2022) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2022.05.12.491602 · Cited by 6
“…short barcode integrants and sgRNAs can now be captured alongside scRNA-seq (Biddy et al., 2018; Dixit et al., 2016; Guo et al., 2019; Rodriguez-Fraticelli et al., 2018; Weinreb et al., 2020) and scATAC-seq (Pierce et al., 2021; Replogle et al., 2020; Rubin et al., 2019) via droplet-based methods.”
BackgroundMethodBarcode-free prediction of cell lineages from scRNA-seq datasetsPreprint
Eisele, A. S., Tarbier, M., Dormann, A. A., Pelechano, V., & Suter, D. M. (2022) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2022.09.20.508646 · Cited by 6
“We used the LARRY barcoding library 17 to transduce mESCs at low multiplicity of infection, sorted barcoded cells (Extended Data Fig.1a-c) and grew them for 48h before scRNA-seq.”
BackgroundMethodInfluentialCell division history encodes directional information of fate transitionsPreprint
Wang, K., Hou, L., Lu, Z., Wang, X., Zi, Z., Zhai, W., He, X., Curtis, C. N., Zhou, D., & Hu, Z. (2022) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2022.10.06.511094 · Cited by 6
“LARRY system (36)) for quantitative cell-fate analysis such as the CoSpar algorithm (37).”
MethodSingle cell susceptibility to SARS-CoV-2 infection is driven by variable cell statesPreprint
Reffsin, S., Miller, J., Ayyanathan, K., Dunagin, M. C., Jain, N., Schultz, D., Cherry, S. R., & Raj, A. (2023) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2023.07.06.547955 · Cited by 6
“A number of such retrospective methods relying on DNA barcoding have been developed recently (Biddy et al., 2018; Emert et al., 2021; Goyal et al., 2021; Jain et al., 2023; Weinreb et al., 2020), but they have not yet been applied to the question of heterogeneity in viral infection.”
Background
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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