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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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
“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.
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 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.
- 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 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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It may state two separate claims as one.
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.
- Based: passes — The paper gives a reason why it holds.
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
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Items 601–700 of 723
Dissecting diversity: clonal analysis of normal and malignant hematopoiesis using cellular barcodes
Jacobs, S. (2021) · University of Groningen research database (University of Groningen / Centre for Information Technology) · doi:10.33612/diss.179996533
Mechanistic gene networks inferred from single-cell data with an outlier-insensitive method
Han, J., Perera, S., Wunderlich, Z., & Periwal, V. (2021) · Mathematical Biosciences · doi:10.1016/j.mbs.2021.108722
Ye, J., Calvo, I. A., Cenzano, I., Vilas‐Zornoza, A., Demorentin, X. M., Lasaga, M., Alignani, D., Pavia, B., Viñado, A. C., Martín‐Úriz, P. S., et al. (2021) · SSRN Electronic Journal · doi:10.2139/ssrn.3969389
Role of government action in improving buildings energy efficiency
Hirst, E., Fulkerson, W., & Carlsmith, R. S. (1981) · OSTI OAI (U.S. Department of Energy Office of Scientific and Technical Information)
Wild, S. A., Cannell, I. G., Kania, K., et al. (2021) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2021.12.09.471927
“…cell transcriptomic profiling, the majority use either lentiviral delivery of a genetic barcode similar to that used here or CRISPR/Cas9-mediate mutations for clonal lineage identification (Biddy et al., 2018; Gutierrez et al., 2021; Quinn et al., 2021; Simeonov et al., 2021; Weinreb et al., 2020).”
BackgroundMethodWang, L., Nie, R., Zhang, J., & Cai, J. (2022) · Research Square · doi:10.21203/rs.3.rs-1763879/v1
Pyro-Velocity: Probabilistic RNA Velocity inference from single-cell dataPreprint
Pinello, L., Bingham, E., Qin, Q., Langenau, D. M., & Manno, G. L. (2022) · Research Square · doi:10.21203/rs.3.rs-2059147/v1
Wollenzien, H., Tecleab, Y. A., Sloane, R. A. S., Restaino, A. C., & Kareta, M. S. (2022) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2022.10.20.513051
“Based on the results of the sequencing, modeling, and previously published work (36), three doublings after barcoding gives sufficient overlap between two independent samples and will be used to generate the barcoded xenografts.”
BackgroundResultHussen, K. A., Chabaane, E., Nelson, E., Lekiashvili, S., Diop, S., Keita, S., Evrard, B., Lardenois, A., Delord, M., Verhoeyen, E., et al. (2022) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2022.12.12.520022
“(GO:0031966) • large ribosomal subunit (GO:0015934) ALCAM, CAT, CD33, CD302, CD55, CEBPB, CHID1, CSF3R, DLK1, FES, GRN, IL6R, IRAK3, KIT, MLEC, MRC2, NFE2, NLN, PRAM1, PRSS21, PTPN7, SPI1, SRGN, TFEC AZU1, CEBPA, CEBPD, CFD, CITED2/4, CLEC11A/12A, CPA3, CST7, CTSG, HGF, IGFBP2, LYZ, MPO, MYB, PRTN3, RHEX, RNASE2, SRGN…”
InfluentialAn Image-Guided Microfluidic System for Single-Cell Lineage TrackingPreprint
Aslan, M. K., Fourneaux, C., Alperen, Y., Stavrakis, S., Parmentier, R., Páldi, A., Gonin-Giraud, S., deMello, J. A., & Gandrillon, O. (2023) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2023.03.31.535033
Ishida, T., Mercoli, J. I., Heck, A. M., Phelps, I. G., Varnum‐Finney, B., Dozono, S., Nourigat-Mckay, C., Kraskouskas, K., Wellington, R., Waltner, O. G., et al. (2023) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2023.06.01.543314 · Cited by 1
CUX1 Regulates Human Hematopoietic Stem Cell Chromatin Accessibility via the BAF ComplexPreprint
Liu, W., Kurkewich, J. L., Stoddart, A., Khan, S. S., Anandan, D., Gaubil, A. N., Wolfgeher, D. J., Jueng, L., Kron, S. J., & McNerney, M. E. (2023) · SSRN Electronic Journal · doi:10.2139/ssrn.4531949
Cell of origin epigenetic priming determines susceptibility to Tet2 mutationPreprint
Schiroli, G., Kartha, V. K., Duarte, F. M., Kristiansen, T. A., Mayerhofer, C., Shrestha, R., Earl, A., Hu, Y., Tay, T., Rhee, C., et al. (2023) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2023.09.04.556230
Holze, H., Talarmain, L., Fennell, K. A., et al. (2023) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2023.11.21.568179
Recent advances in computational modeling
Pinky, P., Neha, N., & Parvez, S. (2024) · Elsevier eBooks · doi:10.1016/b978-0-443-13222-3.00006-x
Zhang, Q., Olofzon, R., Konturek-Cieśla, A., Yuan, O., & Bryder, D. (2024) · eLife · doi:10.7554/elife.91826.2
Dual nuclease single-cell lineage tracing by Cas9 and Cas12aPreprint
Chen, C., Liao, Y., Zhu, M., et al. (2024) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2024.05.27.596125
Transcriptional Regulation of T-Cell Lineage Commitment
Rothenberg, E. V. (2024) · Elsevier eBooks · doi:10.1016/b978-0-128-24465-4.00018-1
Maulding, N. D., Zou, J., Zhou, W., et al. (2024) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2024.06.15.599136
Characterizing Rare Dormant and Cycling Lineages Using the Watermelon System
Handly-Santana, A., & Oren, Y. (2024) · Methods in molecular biology · doi:10.1007/978-1-0716-3882-8_12
Malaviya, R., Laskin, J. D., & Laskin, D. L. (2024) · Elsevier eBooks · doi:10.1016/b978-0-323-95488-4.00129-7
Population-Level Cell Trajectory Inference Based on Gaussian Distributions
Chen, X., Ma, Y., Shi, Y., Fu, Y., Nan, M., Ren, Q., & Gao, J. (2024) · Biomolecules · doi:10.3390/biom14111396
“The mouse pancreatic endocrinogenesis dataset [3], mouse hematopoiesis dataset [20], and human cell cycle dataset [21] are accessible via the NCBI Gene Expression Om-nibus (GEO) repository under accession numbers GSE132188, GSE140802, and GSE146773, respectively.”
Single-nucleus multiple-organ chromatin accessibility mapping in the ratPreprint
Li, R., Yuan, Y., Duan, S., Deng, Q., Ma, W., Liu, C., Gao, P., Li, L., & Liu, C. (2024) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2024.11.11.622900
Hua, J., Wang, K., Chen, Y., Xu, X., Dong, G., Li, Y., Liu, R., Xiong, Y.-C., Ding, J., Zhang, T., et al. (2024) · Clinical and Translational Medicine · doi:10.1002/ctm2.70085 · Cited by 1
Transcriptomique spatiale : de la morphologie aux mécanismes moléculaires
Isnard, P., & Molina, T. J. (2024) · Revue Francophone des Laboratoires · doi:10.1016/s1773-035x(24)00396-4
Self-renewal without niche instruction, feedback or fine-tuningPreprint
MacArthur, B. D., & Greulich, P. (2024) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2024.12.20.629654
Li, J., Wang, Z., Shen, H., & Yuan, Y. (2025) · eLife · doi:10.1101/2024.12.24.630058
“LARRY has been successfully employed to track the in vitro differentiation of human blood cells, accurately capturing lineage trajectories and cell fates ( Weinreb et al., 2020 ).”
TOGGLE Unveils Functional Heterogeneity and Epigenetic Memory in Single CellsPreprint
Chen, J., Sun, T., Song, T., Chen, Z., Xu, H., Guo, Z., Jiang, E., Nong, Y., Yuan, T., Dai, C. C., et al. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.01.01.631041
Clonal memory of colitis accumulates and promotes tumor growthPreprint
Nagaraja, S., Ojeda-Miron, L., Zhang, R., Oreskovic, E., Hu, Y., Zeve, D., Sharma, K., Hyman, R. R., Zhang, Q., Castillo, A., et al. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.02.13.638099
“These tools exogenously introduce unique barcodes that are delivered to individual cells, propagated through cell division and transcriptionally expressed for detection through single-cell RNA-seq 42 .”
InfluentialProbabilistic cell lineage inference from mitochondrial mutationsPreprint
Seidel, S., Hård, J., Ferreira, P., Berkes, P., Jahn, K., Eriksson, C., Mold, J. E., Michaëlsson, J., Kuipers, J., & Beerenwinkel, N. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.02.28.640740 · Cited by 2
“The recent development of advanced molecular tools has enabled combined approaches for lineage tracing with single-cell RNA sequencing (scRNA-seq) to interrogate both cell lineage relationships and cell states [3, 4].”
den Berge, K. V., Bickel, P. J., & Dudoit, S. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.03.19.644088
“However, while TF protein abundance is typically high in single cells, the RNA concentration of the respective TF genes is often low [Weinreb et al., 2020, Chen et al., 2021], and the activity of TF proteins may be modulated through biochemical processes such as phosphorylation [ ?”
Fan, C., Wu, J., Barisas, D. A. G., Xing, X., Kwon, Y., Choi, K., & Wang, T. (2025) · The Journal of Immunology · doi:10.1093/jimmun/vkaf026
scBiopsy-seq: a platform for temporal single-cell RNA-seq analysisPreprint
Cai, L., Lin, S., Qiu, M., Li, L., Li, F., Liu, J., Zou, Y., Xing, N., Liang, S., Xu, X. J., et al. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.03.26.645409
Apoptosis inhibition reprograms alveolar myofibroblasts toward ductal myofibroblasts
Garay, M. J. G., Liu, H., Evans, S. E., et al. (2026) · Cell Reports · doi:10.1016/j.celrep.2026.117891
Ratcliffe, C. D. H., Sparks, H., Boezio, G. L. M., Alexandrov, Y., Jenkins, R. P., Marois, A. L., Soro-Barrio, P., Moughari, F. A., Hulme, H., Lee, R. J., et al. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.06.06.658331 · Cited by 1
How Faithful Flt3-Cre Mouse is to Hematopoietic Lineage Tracing?
Zhai, J., Li, X., Dong, F., & Ema, H. (2025) · Stem Cell Reviews and Reports · doi:10.1007/s12015-025-10930-8
“However, such in vitro barcoding technology requires complex manipulation of cells and is limited to cells that can tolerate separation, short-term culture, and transplantation, making it unsuitable for fragile or niche dependent populations [72].”
Donada, A., Hermange, G., Tocci, T., Midoun, A., Prevedello, G., Abed, L. H., Dupré, D., Sun, W., Milo, I., Bento, S. T., et al. (2026) · Nature Communications · doi:10.1038/s41467-026-75250-y · Cited by 2
“Ourresultscon fi rmthatclonalanalysisbytranscriptomics 3,4,56 – 58 , The existence of clonal memory in healthy haematopoiesis represents a form of non-genetic inheritance under non-pathological conditions.”
Differential Tissue Macrophage Ontogeny and its Impact on Physiology
Jung, S. (2025) · Elsevier eBooks · doi:10.1016/b978-0-12-824465-4.00270-2
Heterogeneity in platelet differentiation pathways
Winroth, A. (2025) · doi:10.69622/29064818
Identification of Distinct Topological Structures From High-Dimensional DataPreprint
Xu, B., & Braun, R. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.08.26.672446
“Life is composed of dynamic processes such as differentiation [1, 2, 3, 4], circadian rhythm [5, 6, 7, 8, 9], cell cycle [10, 11, 12, 13] and so on.”
Choi, H., Jung, S.-E., Earth, Y., Paik, H., Lin, S., Yi, J., Cox, M. J., Oh, S. T., Oh, S. T., & Kang, Y.-A. (2026) · Blood Advances · doi:10.1182/bloodadvances.2026019895 · Cited by 1
barbieQ: an R software package for analysing barcode count data from clonal tracking experiments
Fei, L., Maksimovic, J., & Oshlack, A. (2026) · Bioinformatics · doi:10.1093/bioinformatics/btag397
“2020, Weinreb et al .”
Computational methods to infer lineage decision-making in cancer using single-cell data
Setty, M. N. (2025) · Oxford University Press eBooks · doi:10.1093/med/9780192867636.003.0012
Emerging single-cell technologies and concepts to trace cancer progression and drug resistance
Ahmed, S. S., Pi, D., Bodkin, N., Rebecca, V. W., & Goyal, Y. (2025) · Oxford University Press eBooks · doi:10.1093/med/9780192867636.003.0014
Associative memory in human cancer cellsPreprint
Dayot, S., Morretton, J., Elkhatib, N., et al. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.09.29.679179
“Barcode regions were amplified using primers following the LARRY genomic protocol to specifically target barcode sequences integrated in the genome ( 16 ).”
Allegrezza, A., Beschi, R., Caudo, D., Cavagna, A., Corsi, A., Culla, A., Donsante, S., Giannicola, G., Giardina, I., Gosti, G., et al. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.10.21.683667
QuiCAT: a scalable and flexible framework for mapping synthetic sequences
Lucarelli, D., Kos, T., Shull, C. A., Jiménez, S., Öllinger, R., Rad, R., Saur, D., & Theis, F. J. (2025) · Bioinformatics · doi:10.1093/bioinformatics/btaf607
“Additionally, users can specify a masked pattern using the character “N” when part of the barcode’s internal structure is known, as in LARRY (Weinreb et al .”
InfluentialGene Expression Imaging in Hematopoiesis
Wheat, J. C., Coleman, R. A., & Steidl, U. G. (2025) · Journal of Molecular Biology · doi:10.1016/j.jmb.2025.169545
Tan, Z., Lyu, P., & Jiang, H. (2025) · BMC Genomics · doi:10.1186/s12864-025-12312-3
“Weinreb et al. pointed out that fate bias in multipotent progenitors (MPPs) occurs earlier than the detectable expression of lineage marker genes [69].”
TimeFlow 2: An Unsupervised Cell Lineage Detection Method for Flow Cytometry Data
Liarou, M., Matthes, T., & Marchand‐Maillet, S. (2026) · Cytometry Part A · doi:10.1002/cytoa.70006
PU.1 inhibition sensitizes stem-monocytic AML to BCL2 blockadePreprint
Yashar, W. M., Pacentine, I. V., Taherinasab, A., Nguyen, T., Worme, S. J., Tsuchiya, M., Lusardi, T. A., Hutchinson, C., Fillmore, T., Posso, C., et al. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.01.20.700677
Neural network-assisted RNA velocity imputation for empowering transcript dynamics-based analyses
Egami, R., Shirotori, M., Tamura, T., Oyama, S., & Mizuno, H. (2026) · iScience · doi:10.1016/j.isci.2026.114909
“The mouse hematopoietic lineage dataset is available through Pyro-Velocity, 46 and the original data is derived from Weinreb et al. 18 The mouse lung epithelial dataset is available through CellRank, 47,48 and the original data is derived from Strunz et al. 19 The mouse erythroid lineage, mouse pancreas, and human bon…”
X-CODE: a dual RNA barcoding system for multi-platform clonal tracking and spatial phenotypingPreprint
Rota, R., Flittner, E., Tartagni, M., Bourdim, M., Westlund, E., McKenzie, J. S., Devlin, A. J., Thankamony, A. P., Pandell, N., Gabel, F., et al. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.02.10.705126
scMix: learning temporal dynamics of gene expression under irregular time intervals
Han, S., & Kim, D. (2026) · Bioinformatics · doi:10.1093/bioinformatics/btag080 · Cited by 1
“In addition, each cell is profiled only once, which prevents longitudinal tracking and eliminates the possibility of one-to-one correspondence between cells at different time points (Raj and Van Oudenaarden 2008, Schiebinger et al. 2019, Weinreb et al. 2020).”
A Phospho-Switch for Cell Fate ControlPreprint
ming, J., liu, X., Jia, Z., Shi, W., Li, J., Wang, S., chen, Y., Lin, S., liang, Y., guo, P., et al. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.02.21.707151
Leveraging Twin Information for Inference of Gene Regulatory NetworksPreprint
Arun, K. M., Scher, Y., Zhang, Y. D., Büschel, I., Kuznets-Speck, B., Marr, C., & Goyal, Y. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.02.22.707230
“Finally, we implemented TwINFER on lineage-barcoded single-cell RNA-sequencing (scRNA-seq) dataset [83].”
InfluentialWen, Y., Xiong, J., Gong, F., et al. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.03.10.710716
“Recent advances in single-cell RNA sequencing (scRNA-seq) combined with lineage tracing technologies have enabled the simultaneous capture of cellular gene expression profiles and lineage information [1,2,3].”
Zhong, S., Li, J., Chen, C., Ke, H., Li, Z., Wen, Z., Wu, D., Zeng, C., & Chen, H. (2026) · Spectrochimica Acta Part A Molecular and Biomolecular Spectroscopy · doi:10.1016/j.saa.2026.127767
Pre-existing cell states predict resistance to multiple treatments
Schaff, D. L., White, P. E., Cote, C. J., Watterson, G., Lin, K. Z., Fasse, A. J., Zhang, N. R., & Shaffer, S. M. (2026) · Cell Genomics · doi:10.1016/j.xgen.2026.101191 · Cited by 2
“While recent advances combining clonal tracing with scRNA-seq have yielded valuable insights into resistance phenotypes, 17,19,26–29 (legend continued on next page) treatment.”
Quantifying Haematopoietic Cell Dynamics
Hustin, L. (2022) · theses.fr (ABES) · doi:10.70675/48546646z0fa0z4e9bzb5a0zb1067f26721d
Modelling the geometry of high-dimensional omics data : dimensionality, subspaces, trajectories
Bac, J. (2022) · theses.fr (ABES) · doi:10.70675/3c682a3dz1b61z4109z9dbdz18ac2365aed0
Deciphering the impact of perturbations in developmental hematopoiesis
Sommer, A. (2024) · theses.fr (ABES) · doi:10.70675/10ab0f8az3243z40b8z8d8ezdccd2af897af
Co-option of lineage plasticity as a hallmark of multipotent acute leukemias
Gutierrez, A., & Kentsis, A. (2026) · Blood Neoplasia · doi:10.1016/j.bneo.2026.100231 · Cited by 1
SCOPE: Localizing fate-decision states and their regulatory drivers in single-cell differentiationPreprint
Zhao, Y., Finkbeiner, C., Setty, M. N., & Lin, K. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.04.07.717037
“Applications across simulations, lineage-traced mouse hematopoiesis [20], and diverse human multi-omic datasets demonstrate that SCOPE reliably recovers biologically meaningful checkpoints and their molecular drivers.”
Meng, Y., Sultan, R., Jiang, P., Wang, Q., Tan, D., Wang, J., Li, P., & Chen, L. (2026) · Physical review. E · doi:10.1103/xwt5-rmjk
Kamran, M., Nassar, N. N., & Cancelas, J. A. (2026) · Open Biology · doi:10.1098/rsob.260011
Clonal analysis for understanding fate biases in developing embryos
Isaev, S. V., & Adameyko, I. I. (2026) · Current Opinion in Genetics & Development · doi:10.1016/j.gde.2026.102475 · Cited by 1
Lutz, M. W., Man, Z., Zheng, Y., et al. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.04.30.721959
Understanding development and cellular plasticity in the pancreas in health and disease
Torres-Cano, A., & Spagnoli, F. M. (2026) · Nature Reviews Gastroenterology & Hepatology · doi:10.1038/s41575-026-01211-x · Cited by 1
Metz, J., Greco, A., Frank, L., Claudino, N., Rodewald, H.-R., & Höfer, T. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.05.10.724120
Human CD34 as a biomarker and functional tool in progenitor cell isolation: A comprehensive review
Woodard, G. E. (2026) · Transfusion and Apheresis Science · doi:10.1016/j.transci.2026.104454
Zhou, D., Li, Y., Yan, Z., Huang, J., Bao, Y., Gao, C., Tang, C., Wu, X., Wang, S., Tang, D., et al. (2026) · Nature Communications · doi:10.1038/s41467-026-73632-w
“To explore how recurrent tumours were generated, we used a statistic termed “ Evolutionary Coupling ” (hereafter referred to as EvoCoupling) that extends a clonal coupling statistic 29,41 to quantify the phylogenetic distance between pairsofcellstates,whichcanbe usedtoinferevolutionary trajectories.”
Computational blueprints for cell fate programming
Yang, P. (2026) · Stem Cell Reports · doi:10.1016/j.stemcr.2026.102929
Wang, J., Li, R., Guo, C., Qiang, M., Wang, S., Wang, G., Tu, K., & Xu, Y. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.06.09.731114
Schwartz, M. A., Lue, D., Mutaher, M., McNerney, M., Olajide, O., Mehta, A., Rowe, J. H., Park, R. J.-H., Malehmir, M., Dubrot, J., et al. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.06.14.732162
Steller: a high-resolution platform for spatial clonal tracing of mammalian brain developmentPreprint
Li, Z., Wang, C., Guo, C., Liao, Y., Chen, C., Xing, Q., Pei, W., & Peng, G. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.06.16.732634
“CloneBC plasmid library generation LARRY barcode library was generated following Weinreb et al., 2020 18 with modification.”
Heritable single-cell gene expression states shape functional variability in innate immune responsesPreprint
Redondo, J. A., Co, N. T., Alachkar, N., Moran, J. C., Srivastav, A., Kochańczyk, M., Muldoon, M. R., Roberts, I. S., Krawczyk-Balska, A., Singh, A., et al. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.06.17.732820
Lutz, M. W., Man, Z., Zheng, Y., Venkatesan, S., & Chiba‐Falek, O. (2026) · Alzheimer s & Dementia Translational Research & Clinical Interventions · doi:10.1002/trc2.70273
“22 ThisworkaimedtopredictADdisease (2) we used the diagnostic labels of normal cognition, MCI, and AD, whereas Huang et al. used labels of control, asymptomatic AD, typical AD, and low neurofibrillary tangle AD; (3) Huang reported 40% to 80% label transfer accuracy for the blood samples: using their optimal transport…”
Lineage tracing in human cortical development
Yuan, G., Steyert, M. R., & Nowakowski, T. J. (2026) · Current Opinion in Genetics & Development · doi:10.1016/j.gde.2026.102506 · Cited by 1
scPD: a Python package for inferring continuous population dynamics from single-cell snapshot data
Yin, Y., Qi, H., & Hu, H. (2026) · Bioinformatics Advances · doi:10.1093/bioadv/vbag188
“1d), consistent with an energy-minimization landscape (Wang et al. 2011, Weinreb et al. 2020).”
The development of single-cell lineage tracing technology and its application in immunotherapy
Wang, J., Li, Z., Chen, J., Li, H., Chen, F., Tan, L., Chen, X., Liu, S., Zhang, W., & Shao, H. (2026) · Molecular Therapy Advances · doi:10.1016/j.omta.2026.201802
“40,56 Taha B. Hayal and colleagues incorporated a highly complex genetic barcode library into a CAR lentiviral vector, prepared barcode-labeled CAR-NK cells in vitro , and used them to track CAR-NK cells in NSG mice.”
Inferring differentiation trajectories from T cell clonotype distributionsPreprint
Guryleva, M. V., Danielsson, M., Tibbitt, C. A., Coquet, J. M., & Murrell, B. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.06.29.735205
Tykocinski, M. L. (2026) · American Journal Of Pathology · doi:10.1016/j.ajpath.2026.06.005
Rediscovering eosinophil identity through tissue adaptation
Chakarov, S., & Xu, H. (2026) · Nature Immunology · doi:10.1038/s41590-026-02593-5 · Cited by 3
“…systems have refined this classical hierarchy by revealing early bifurcation of hematopoietic trajectories, separating a neutrophil–monocyte branch from a GATA-binding factor 1 (GATA-1)-associated lineage that includes eosinophils, basophils, mast cells and erythro-megakaryocytic lineages 23–25 .”
FIERCE: reconstructing dynamic trajectories from the differentiation potency of single cells
Calderoni, L., Romano, O., Grandi, F., Bicciato, S., & Forcato, M. (2026) · Bioinformatics · doi:10.1093/bioinformatics/btag516
“We downloaded the fastq files of the in vitro murine hematopoiesis dataset sequenced by Weinreb et al. (2020) with the LARRY protocol from the Sequence Read Archive (SRX7199569).”
InfluentialRühle, J., Frömel, R., Bernal, A., Szu‐Tu, C., Bowness, J. S., & Velten, L. (2026) · Genome biology · doi:10.1186/s13059-026-04186-9 · Cited by 1
“On day 21, cells were transferred into differentiation medium, enabling differentiation across the pan-myeloid spectrum [18, 41].”
Cell lineage tracing: How DNA-barcoding technologies reveal the secrets of ontogenesis
Drach, M., Shkarina, L. N., Бозов, К. Д., Primak, A. L., & Karagyaur, M. N. (2026) · Регенерация органов и тканей. · doi:10.60043/2949-5938-2026-1-6-23
Pre-Existing Heterogeneity Predicts Rare Proteostasis-Stress Programs Across Diverse Perturbations
Lyu, Z., Shao, C., Yang, R., Yu, Q., Yang, G., & Wang, Z. (2026) · Biology · doi:10.3390/biology15151230 · Cited by 2
Hjorthaug, H. S., Heimli, M., Flåm, S. T., & Lie, B. A. (2026) · iScience · doi:10.1016/j.isci.2026.116916
“Notably, progenitor populations are reported to possess heterogeneity not explained by gene expression, 63,64 and we hypothesize that the mcTEC clusters represent different chromatin states destined for distinct differentiation paths.”
Reconstructing whole-organism cell phylogenies with resolved ancestral transcriptional statesPreprint
Liu, Z., Deng, S., Deng, S., Zhang, M., Liu, B., Xiang, H., Chen, Z., Zhang, A., Shendure, J., & He, X. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.07.30.741939
Mapping cell fate in space and time
Stuart, H., & McKenna, A. (2026) · Cell stem cell · doi:10.1016/j.stem.2026.07.001
Move BeTween modAlities (MBTA) employs flow matching to predict single cell data modalitiesPreprint
Xu, B., Zhang, Y., & Michor, F. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.08.05.743110
“…single-cell RNA sequencing (scRNA-seq) remains the most prevalent, with large numbers of cells measured in each scRNA-seq experiment, allowing for the identification of hidden low-dimensional structures [8, 9, 10, 11] such as trees commonly observed in differentiating systems [12, 13, 9].”
Wang, G., Meng, Y., & Han, J. (2026) · Ageing Research Reviews · doi:10.1016/j.arr.2026.103293
Cell fate decision and lineage specification mechanisms in hematopoiesis
Lee, I., Earth, Y., & Kang, Y.-A. (2026) · Current topics in developmental biology/Current Topics in Developmental Biology · doi:10.1016/bs.ctdb.2026.06.001
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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