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Spatiotemporal transcriptomic atlas of mouse organogenesis using DNA nanoball-patterned arrays
Open accessPublished version of a preprint
Abstract
Spatially resolved transcriptomic technologies are promising tools to study complex biological processes such as mammalian embryogenesis. However, the imbalance between resolution, gene capture, and field of view of current methodologies precludes their systematic application to analyze relatively large and three-dimensional mid- and late-gestation embryos. Here, we combined DNA nanoball (DNB)-patterned arrays and in situ RNA capture to create spatial enhanced resolution omics-sequencing (Stereo-seq). We applied Stereo-seq to generate the mouse organogenesis spatiotemporal transcriptomic atlas (MOSTA), which maps with single-cell resolution and high sensitivity the kinetics and directionality of transcriptional variation during mouse organogenesis. We used this information to gain insight into the molecular basis of spatial cell heterogeneity and cell fate specification in developing tissues such as the dorsal midbrain. Our panoramic atlas will facilitate in-depth investigation of longstanding questions concerning normal and abnormal mammalian development.
Overview
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TL;DR
Stereo-seq combines DNA nanoball-patterned arrays with in situ RNA capture to achieve large field-of-view spatial transcriptomics at cellular resolution. The authors use it to build MOSTA, a mouse organogenesis atlas spanning E9.5–E16.5, and demonstrate analyses of tissue patterning, cell heterogeneity, and developmental disease gene expression.
Key findings
- Stereo-seq captured 69 UMI on average per 2 µm bin, 1,450 per 10 µm bin, and 133,776 per 100 µm bin in mouse olfactory bulb, outperforming Seq-Scope’s 848 UMI per 10 µm bin; adjacent sections were highly consistent (R² = 0.963).
- MOSTA profiled 53 sagittal sections from C57BL/6 mouse embryos at E9.5–E16.5, yielding 3,511,403 bins; average captured genes per bin ranged from 1,770 at E15.5 to 3,900 at E10.5, and UMI from 4,357 at E15.5 to 13,789 at E9.5.
- In an E16.5 embryo section, image-based segmentation yielded 281,377 cells with an average of 1,107 UMI and 529 genes per cell, resolving 25 major cell types; adult mouse hemibrain segmentation yielded 50,140 cells with an average of 1,910 UMI and 792 genes per cell.
- Dorsal midbrain analysis showed spatially heterogeneous progenitor organization: radial glia were most abundant at E12.5, declined rostrally by E14.5, and concentrated caudally by E16.5; HES1 regulon was active in glioblasts and HES6 in neuroblasts. Over 10% of captured transcripts contained intronic reads, enabling spatial RNA velocity of interneuron migration.
Methods
- Stereo-seq uses DNB-patterned arrays with random barcodes, in situ sequencing to assign coordinate identities, and UMI-polyT capture probes; 10 µm frozen tissue sections are fixed, permeabilized, reverse transcribed, amplified, and sequenced.
- Samples included adult mouse olfactory bulb, adult coronal hemibrain, and C57BL/6J embryos from E9.5 to E16.5; 53 sagittal embryo sections were profiled, including 18 E16.5 sections (13 from one embryo and 5 from another).
- Analyses used the SAW pipeline with STAR alignment to mm10 and UMI collapsing, spatially constrained clustering (SCC), image-based cell segmentation, SCENIC, Hotspot, Tangram, dynamo, and monocle 3.
Limitations
- The paper states that cell segmentation may be imperfect when multiple cell types are close, especially smaller cell types like immune cells, and that low-expression genes may be missed due to capture limitations.
- The paper states that 53 sections covering 23.4 cm² may not fully cover all regional tissue types; incomplete sampling could affect inference of anatomic relationships and developmental trajectories, and 3D transcriptomic rendering is not possible with the current dataset.
- The paper states that functional validations are needed and that additional algorithms may be required for broader applications; evident from the text, the atlas is mouse-only and disease-gene projections are correlative rather than experimentally validated in this study.
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“Here, we combined DNA nanoball (DNB)-patterned arrays and in situ RNA capture to create spatial enhanced resolution omics-sequencing (Stereo-seq).”
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In the paper
“Stereo-seq maps the spatiotemporal transcriptomic dynamics during mouse organogenesis”
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“In the E16.5 embryo, we could define the localization of regulons across serial sections of the same organ such as GATA4 and TBX5 in the ventriculus and atrium of the developing heart, respectively (Figure 3F).”
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“We also reconstructed the developmental trajectories for the annotated regions in whole embryos using TOME (Qiu et al., 2022a). This showed spatiotemporal connections largely consistent with our contemporary understanding of mouse development (Figure S2D).”
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- Not checked yetcites Qiu et al., 2022a
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“The expression of many known marker genes in these brain clusters is consistent with known cellular distributions across the corresponding anatomic regions (Figures S3C and S3D).”
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“Remarkably, the external boundaries of these clusters closely resembled the anatomic regions (Figures 3A and S2C and MOSTA website).”
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“Stereo-seq captured UMI counts ranging on average from 69 per 2 m m (diameter) bin (bin 3, 3 3 3 DNB), 1,450 per 10 m m (diameter) bin (bin 14, 14 3 14 DNB, equivalent to 1 medium size cell), and 133,776 per 100 m m (diameter) bin (bin 140, 140 3 140 DNB) (Figure 1C).”
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“Tissue-specific identities were confirmed by visualizing specific marker genes (e.g., Acta2 in the smooth muscle, Myog in the skeletal muscle, Krt5 in the epidermis, Sftpc in the lung, Afp in the liver, and Col2a1 in the cartilage primordium) on the spatial maps across all embryonic time points (Figure S3A).”
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“The distribution of genes and UMI per bin was highly consistent between adjacent sections (R 2 = 0.963) (Figures S1B and S1C).”
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“Notably, the extended coverage of the E16.5embryoallowedustoobtainserialimagesindevelopingtissues and organs, such as the meninges ( Atp1a2 + ), the spinal cord ( Hoxb8 + ), and the lung ( Sftpc + ) (Figure S3B).”
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“For example, the epithelial cell cluster ( Epcam + , Krt8 + ) could be grouped into specialized epithelial cell subtypes including hair follicle cells ( Krt5 + , Krt17 + ), alveolar type II cells ( Foxp2 + , Nkx2-1 + ), club cells ( Scgb3a2 + , S100a6 + ), and ciliated cells ( Dynlrb2 + , Foxj1 + ) from the lung”
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“There was substantial colocalization of the dye and the aggregated transcripts detected by Stereo-seq (Figures 2A and 2B), illustrating that cells can be segmented in an image-dependent manner.”
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“After filtering out the low-capture cells, we obtained 50,140 segmented cells with an average of 1,910 UMI and 792 genes per cell (Figure S1F).”
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“For E9.5-E15.5 stages, four to six sections were included from different embryos. For E16.5, 18 sections were profiled, with a series of 13 sections from one single embryo and five from another to assess reproducibility, allowing coverage of all major tissues and organs (Table S1).”
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“Among the E16.5 regulons, ARNT2 was enriched in the brain, FOXC2 in the meninges, GATA4 in the heart, SOX9 in the bone, and MEF2C in the muscle (Figure 3E).”
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“Importantly, Stereo-seq has a larger number of spots per 100 m m 2 , with smaller spot size and center-to-center distance, than any other published method (Figure 1B).”
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“For example, at E9.5, we identified 31 modules containing a total of 498 regulons, and at E16.5, we identified 35 modules containing a total of 469 regulons that correspond to different tissues (Figures 3D, S4F, and S4G).”
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“For example, in the E13.5 spinal cord, we identified the ventricular zone ( Hopx + ), marginal zone ( Slc5a7 + ), basal plate ( Vsnl1 + ), ventral ( Fut9 + ) and lateral parts of the spinal alar plate ( Hoxb8 + ), and superficial stratum of spinal basal plate ( Pdyn + ); in the E16.5 kidney, we localized artery ( Myl9 + ), cortex ( Lhx1 + ), medulla ( Nrxn3 + ), capsule ( Robo2 + ), and glomeruli”
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“Here, we report the development of a DNB-based genome-wide technology, Stereo-seq, that combines single-cell resolution, high sensitivity, and a large field of view”
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“Theaveragenumberofcapturedgenes per bin ranged from 1,770 at E15.5 to 3,900 at E10.5 and of UMI from 4,357 at E15.5 to 13,789 at E9.5 (Figure S2A; Table S1).”
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“In addition, since Stereo-seq captures high density of signals, we performed spatial reclustering of bins from areas corresponding to selected tissues, which revealed detailed subregions.”
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“Reclustering of specific cell populations could further identify cell subtypes or cell states.”
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“After filtering out the low-capture cells, we obtained 281,377 segmented cells with an average of 1107 UMI and 529 genes per cell (Figures 4A and S5A).”
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“These cell types were either located in specific anatomic regions (e.g., cardiomyocytes, hepatocytes, and different types of neurons) or represented common cell types spread across multiple sites (e.g., chondrocytes, epithelial cells, myoblasts, and endothelial cells).”
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“Thedistribution of genes and UMI perbin varied between sections from different time points (Figure S2B).”
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“So far, we have used Stereo-seq chips of up to an effective area of 13.2 cm 3 13.2 cm for different tissue sizes, subdivided into 50, 100, and 200 mm 2 chips for profiling sections from the mouse olfactory bulb ( 10.5 mm 2 ), a mouse hemibrain ( 24.2 mm 2 ) and whole mouse embryos ( 7.1 to 76.1 mm 2 ), respectively (Figure S1A).”
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“The distribution of specific markers ( Pcp4 and Slc17a7 ) captured by Stereo-seq exhibited the expected similarity to in situ hybridization (ISH) data taken from Allen Brain Atlas (ABA) (Lein et al., 2007) and clearer patterns than HDST (Vickovic et al., 2019) or Slide-seqV2-resolved (Stickels et al., 2021) expression data (Figure S1E).”
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“The resulting gene programs showed well-defined spatial patterns that, to a large extent, match anatomic regions and contained sets of genes that could be validated by ISH (e.g., Kitl colocalizing with Shh in the E9.5 notochord and Pantr1 with Sox2 in the E11.5 brain) (Figures S4A-S4E).”
What the sources say
- Not checked yetcites DeTomaso and Yosef, 2021
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- Rests on an assumption: Amalgamation of different signals in complex tissues can complicate interpretations.
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“Standard DNB chips have spots with approximately 220 nm diameter and a center-to-center distance of 500 or 715 nm (Figure 1A, step 1), providing up to 400 spots for tissue RNA capture per 100 m m 2 .”
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“We observed high Wnt5a expression in mesenchymal cells and fibroblasts from the maxilla, whereas in the limb, it was located mainly in mesenchymal cells (Figures 6C and 6D).”
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“HES6 was highly active in neuroblasts, and its target genes included Ascl1 and Hes5 (Figures 5L and S7G).”
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“We also observed unknown potential regulators ( Ttc3 and Celf4 ) of interneuron migration that could be interesting for further exploration (Figure 4F).”
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“In addition, we identified highly specific expressions of extrinsic cues (ligands) in glioblasts (e.g., Rspo1/2/3 , Wnt1 , and Wnt9a ) and neuroblasts ( Fgf15, Wnt7b, and Tnc ) (Figures 5G-5I and MOSTA website), providing potential mechanistic insight for the further specification of these progenitors.”
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“Moreover, Stereo-seq is an affordable technology.”
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“At E14.5, the radial glia cells disappeared from the rostral part and remained in the more caudal domains of the ventricular zone, revealing the spatiotemporal development of this brain region.”
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“We observed that Wnt5a localized in the craniofacial region, limb, genital ridge, and brain (floor plate and neocortex) from multiple developmental stages (Figure 6B), consistent with the disease phenotype (Menezes et al., 2010).”
What the sources say
- Not checked yetcites Menezes et al., 2010
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“The resulting developmental trajectories revealed two branches arising from radial glia cells, one to neuroblasts andthe other to glioblasts (Figure 5D).”
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“The high resolution of Stereo-seq allows efficient image-based cell segmentation, but in some cases, where multiple cell types are close to each other, particularly smaller cell types like immune cells, cell segmentation may be imperfect.”
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“For example, HES1 was highly active in glioblasts, and its target genes included known genes specific to the glia lineage such as Aldh1l1 and Nfib .”
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“Radial glia cells ( Mki67 + , Dbx1 + ), which are the source of both neuroblasts ( Npas3 + , Adgrb3 + ) and glioblasts ( Aldh1l1 + , Efna5 + ), were the most abundant progenitors at E12.5, with widespread distribution.”
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“Similarly, the MSX1 regulon was more enriched in mesenchymal cells and fibroblasts in the maxilla, and mesenchymal cells in the limb (Figures 6C and 6D).”
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“Msx1 was expressed at different levels in multiple cell types of the maxilla including mesenchymal cells, but in the limb, it was rather restricted to the latter cell type only.”
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“The current DNB capture chip costs 220 RMB/mm 2 ($35/ mm 2 ) or 22,000 RMB/cm 2 ($3,500/cm 2 ).”
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“Similarly, although many genes were mainly enriched in one tissue (in some cases, they were confined to a specific region) or system, others were more widely distributed.”
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“Our E16.5 Stereo-seq map clearly identified the expression of interneuron progenitor markers ( Dlx1 + , Dlx2 + , Lhx6 + , Nkx2-1 + ) (Lim et al., 2018) within the medial ganglionic eminence (MGE) and the migratory trajectory of interneurons into the cerebral cortex (Figures 4D and S6D).”
What the sources say
- Not checked yetcites Lim et al., 2018 source
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- Rests on an assumption: Dorsal cortical ventricular zones give rise to glutamatergic neurons that populate the cerebral cortex and hippocampus, and ventral ventricular zones of the lateral, medial, and caudal ganglionic eminence give rise to GABAergic interneurons that populate the striatum and globus pallidus and migrate into the cerebral cortex.
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In the paper
“In this regard, over 10% of the Stereo-seq captured transcripts contained intronic reads (Figure S6E), comparable with the proportion captured by scRNA-seq (La Manno et al., 2018; Qiu et al., 2022b).”
What the sources say
- Not checked yetcites La Manno et al., 2018 source
- Not checked yetcites Qiu et al., 2022b
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“Similarly, Stereo-seq has a genome-wide coverage, but due to limitations in capture, relevant genes with low expression may be missed.”
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“Some genes were expressed in most or all embryonic stages, whereas others had a restricted window.”
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In the paper
“At this stage, there was only occasional appearance of sparsely distributed neuroblasts, whereas glioblasts localized mostly in the central and caudal parts.”
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“Although the function of many of the transcription factors controlling these regulons has been previously reported, we have identified the spatial domains in which they act.”
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In the paper
“Importantly, Stereo-seq also captures intronic transcripts, making it possible to calculate spatial RNA velocity with singlecell resolution, which as in scRNA-seq studies (La Manno et al., 2018; Qiu et al., 2022b), can facilitate the study of cell fate transitions.”
What the sources say
- Not checked yetcites La Manno et al., 2018 source
- Not checked yetcites Qiu et al., 2022b
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None of the sources cited for this claim have been checked yet.
In the paper
“As informative examples, NKX2-1 , which is mutated in brainlung-thyroid syndrome (Shetty et al., 2014), was mainly enriched in brain (hippocampus and subpallium) and lung and Lmod3 , which is associated with nemaline myopathy (Berkenstadt et al., 2018), was enriched in the skeletal muscle (Figure 6B).”
What the sources say
- Not checked yetcites Shetty et al., 2014
- Not checked yetcites Berkenstadt et al., 2018
Strength:plain· not yet calibratedCausal:correlational· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passesIt 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
“The high-density RNA capture with DNB chip requires a sequencing depth of around 150 gigabases/cm 2 , which costs 5,000 RMB ( $800) based on current short-read sequencing platforms.”
Strength:plain· not yet calibratedCausal:direct· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✗ Based: failsProbably not a substantial claim. The paper’s conclusions may not depend on it. The paper may give no reason why it holds. It may state two separate claims as one.
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
“At E16.5, radial glia cells further decreased and concentrated in the caudal part, with neuroblasts showing a more restricted distribution and glioblasts expanding in number in the original locations.”
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt 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
“(F) Heatmap showing the regulons with significant spatial autocorrelation grouped into different modules based on pairwise spatial correlations of an E9.5 section (E1S1).”
Tests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably not a substantial claim. The paper’s conclusions may not depend on it.
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
“(A) Stereo-seq chips of different sizes ranging from 50 mm 2 to 174.24 cm 2 .”
Strength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably not a substantial claim. The paper’s conclusions may not depend on it.
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 and B) Heatmaps showing the genes with significant spatial autocorrelation grouped into different gene modules based on pairwise spatial correlations of E9.5 (left, E1S1) and E16.5 (right, E1S1) sections.”
Strength:plain· not yet calibratedCausal:correlational· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably not a substantial claim. The paper’s conclusions may not depend on it.
The paper 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
“Importantly, we have profiled a total of 53 sagittal sections corresponding to an overall area of 23.4 cm 2 , but more sections may still be necessary to provide adequate coverage of all regional tissue types.”
Strength:hedged· not yet calibratedCausal:conditional· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passesIt may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Hence, Stereo-seq spatially characterizes the transcriptomic organization and individual cell-type composition of complex tissues in an unbiased manner with high sensitivity and large field of view.”
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
None of the sources cited for this claim have been checked yet.
In the paper
“Recent cell atlases using high-throughput single-cell RNA-sequencing (scRNA-seq) and single-cell assay for transposase accessible chromatin sequencing (scATAC-seq) technologies have provided an increasingly detailed view of mouse developmental gene expression and gene regulatory dynamics”
What the sources say
- Not checked yetcites Cao et al., 2019 source
- Not checked yetcites He et al., 2020
- Not checked yetcites Pijuan-Sala et al., 2020
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesProbably 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
“Therefore, Stereo-seq data can be used to both identify functional anatomic regions in large tissues through bin-based clustering, and cell types based on image-guided cell segmentation.”
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
In the paper
“The colocalization of Pantr1 with Sox2 is consistent with the regulatory function of this long noncoding RNA in early neuronal differentiation (Goff et al., 2015).”
What the sources say
- Not checked yetcites Goff et al., 2015
Strength:plain· not yet calibratedCausal:correlational· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably not a substantial claim. The paper’s conclusions may not depend on it.
In the paper
“These data can be extracted from scRNA-seq datasets using algorithms such as single-cell regulatory network inference and clustering (SCENIC) (Aibar et al., 2017), but the lack of spatial information is likely to confound important associations.”
What the sources say
- Not checked yetcites Aibar et al., 2017 source
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
- Rests on an assumption: The lack of spatial information in scRNA-seq data is likely to confound important associations in gene regulatory network analysis.
In the paper
“Kitl is a pleiotropic factor that acts in the ontogeny of different cell types (Broudy, 1997).”
What the sources say
- Not checked yetcites Broudy, 1997
Strength:plain· not yet calibratedCausal:direct· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably not a substantial claim. The paper’s conclusions may not depend on it.
None of the sources cited for this claim have been checked yet.
In the paper
“Early mouse embryos (Lohoff et al., 2022; Peng et al., 2019), specific late-stage-embryonic tissues such as the cerebral cortex (Di Bella et al., 2021; Stickels et al., 2021), and whole midgestation embryos (Liu et al., 2020; Srivatsan et al., 2021; Yao et al., 2020) have been analyzed using these approaches.”
What the sources say
- Not checked yetcites Lohoff et al., 2022
- Not checked yetcites Di Bella et al., 2021
- Not checked yetcites Liu et al., 2020 source
Load-bearingStrength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesProbably not a substantial claim. 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
“Stereo-seq defines the spatiotemporal window of developmental disease vulnerability”
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✗ Based: failsProbably not a substantial claim. The paper may give no reason why it holds.
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 demonstrate the utility of MOSTA for defining the spatiotemporal windows of disease-related gene expression during development and for establishing potential vulnerabilities.”
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt 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
“Stereo-seq is a DNB-based spatially resolved transcriptomic technology with genome-wide coverage, cellular resolution, high sensitivity, and large field of view.”
Load-bearingStrength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✗ Based: failsProbably not a substantial claim. The paper may give no reason why it holds. 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
“These parameters are all critical for accurately profiling the transcriptomic heterogeneity of relatively large mid- and late-stage mammalian embryos.”
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✗ Based: failsProbably not a substantial claim. The paper may give no reason why it holds.
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
“Due to its characteristics and applicability, Stereo-seq also has the potential to move into routine clinical practice as an extraordinary diagnostic tool complementary to medical imaging and histopathology data.”
Strength:hedged· not yet calibratedCausal:conditional· not yet calibratedTests✓ Asserted: passes✗ Checkable: fails✗ Load-bearing: fails✗ Based: failsProbably not a substantial claim. It may be too vague to check against evidence. The paper’s conclusions may not depend on it. The paper may give no reason why it holds.
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
“MOSTA comprehensively characterizes cell-type and cellstate heterogeneity at whole embryo level, which is relevant for reconstructing cell differentiation processes involving regional and subregional specificities.”
Load-bearingStrength:boosted· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
- Rests on an assumption: The cell capture bias and lack of contextual microenvironment limits the power of scRNA-seq for predicting the origin and mechanisms of genetic diseases.
In the paper
“We then investigated Msx1 , a homeobox gene encoding a transcription factor with known crosstalk with WNT5A (Lin et al., 2011).”
What the sources say
- Not checked yetcites Lin et al., 2011
Strength:boosted· not yet calibratedCausal:correlational· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
In the paper
“Nowadays, scRNA-seq datasets are becoming widely used to predict the origin and mechanisms of genetic diseases including developmental disorders (Watanabe et al., 2019).”
What the sources say
- Not checked yetcites Watanabe et al., 2019
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesProbably 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
“Besides the study of development, Stereo-seq and its future refinements have the ability to transform multiple other research fields.”
Strength:plain· not yet calibratedCausal:conditional· not yet calibratedTests✓ Asserted: passes✗ Checkable: fails✗ Load-bearing: fails✗ Based: failsProbably not a substantial claim. It may be too vague to check against evidence. The paper’s conclusions may not depend on it. The paper may give no reason why it holds.
In the paper
“The dorsal midbrain contains complex multilayered structures like the cerebral cortex that form the superior and inferior colliculi (Arimura et al., 2019).”
What the sources say
- Not checked yetcites Arimura et al., 2019
Strength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably not a substantial claim. The paper’s conclusions may not depend on it.
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
“Mice and humans differ in multiple aspects of normal physiology and disease; hence, there are caveats, but this approach clearly has the potential to uncover disease mechanisms.”
Strength:boosted· not yet calibratedCausal:conditional· not yet calibratedTests✓ Asserted: passes✗ Checkable: fails✗ Load-bearing: fails✓ Based: passesProbably not a substantial claim. It may be too vague to check against evidence. The paper’s conclusions may not depend on it. It may state two separate claims as one.
- Rests on an assumption: Mice and humans differ in multiple aspects of normal physiology and disease.
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 findings suggest an asynchronous and spatially heterogeneous model of neurogenesis and gliogenesis, with glioblasts appearing early at specific anatomic locations where they remain until later differentiation.”
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
- Rests on an assumption: The development of the dorsal midbrain has been less well characterized in terms of cellular taxonomy and organization compared with other brain regions.
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
“All relevant procedures involving animal experiments presented in this study are compliant with ethical regulations regarding animal research and were conducted under the approval of the Animal Care and Use committee of the Guangzhou Institutes of Biomedicine and Health, Chinese Academy of Sciences (license number IACUC2021002).”
Strength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably not a substantial claim. The paper’s conclusions may not depend on it.
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
“Reads with MID containing either N bases or more than 2 bases with quality score lower than 10 were filtered out.”
Strength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably not a substantial claim. The paper’s conclusions may not depend on it.
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
“UMI with the same CID and the same gene locus were collapsed, allowing 1 mismatch to correct for sequencing and PCR errors.”
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesProbably 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
“The whole procedure was integrated into a publicly available pipeline SAW available at https://github.com/BGIResearch/SAW.”
Strength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably not a substantial claim. The paper’s conclusions may not depend on it.
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
“All relevant procedures involving animal experiments presented in this study are compliant with ethical regulations regarding animal research and were conducted under the approval of the Animal Care and Use committee of the Guangzhou Institutes of Biomedicine and Health, Chinese Academy of Sciences (license number IACUC2021002).”
Strength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably not a substantial claim. The paper’s conclusions may not depend on it.
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
“Finally, although we have developed algorithms including the SCC for the analyses in this study, others may be needed for a broader range of applications in various tissues from different species.”
Strength:hedged· not yet calibratedTests✓ Asserted: passes✗ Checkable: fails✗ Load-bearing: fails✗ Based: failsProbably not a substantial claim. It may be too vague to check against evidence. The paper’s conclusions may not depend on it. The paper may give no reason why it holds.
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
“For Figure 1C, to ensure that proper comparisons were made, the data of Stereo-seq were binned into bin 3 (3 3 3 DNB, 2 m m), bin 14 (14 3 14 DNB, 10 m m) or bin 140 (140 3 140 DNB, 100 m m).”
Load-bearingStrength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesProbably 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
“CID sequences on the first reads were first mapped to the designed coordinates of the in situ captured chip achieved from the first round of sequencing, allowing 1 base mismatch to correct for sequencing andPCRerrors.”
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesProbably not a substantial claim.
In the paper
“To validate the cell type identity characterized in our adult mouse hemibrain section, annotated scRNA-seq profiles of the adult mouse brain downloaded from Linnarsson Lab website (http://mousebrain.org/adolescent/) (Zeisel et al., 2018) were used for comparison.”
What the sources say
- Not checked yetcites Zeisel et al., 2018 source
Load-bearingStrength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesProbably not a substantial claim.
The paper relies on this without support.
The lack of topographical information greatly complicates interpretations of the hierarchical mechanisms underlying the emergence of cell positioning and identities.
“However, the lack of topographical information greatly complicates the interpretations of the hierarchical mechanisms underlying the emergence of cell positioning and identities.”
The paper relies on this without support.
The laboratory mouse is an excellent model animal to study development and establish parallels with larger mammalian species.
“The laboratory mouse is an excellent model animal to study development and establish parallels with larger mammalian species.”
The paper relies on this without support.
The lack of spatial information in scRNA-seq data is likely to confound important associations in gene regulatory network analysis.
“but the lack of spatial information is likely to confound important associations.”
The paper relies on this without support.
Amalgamation of different signals in complex tissues can complicate interpretations.
“This is important because amalgamation ofdifferent signals in complex tissues can complicate the interpretations.”
The paper relies on this without support.
The precise regulation of cell identity in the developing brain is critical for the establishment of brain architecture but is yet poorly understood.
“The precise regulation of cell identity in the developing brain is critical for the establishment of brain architecture but is yet poorly understood.”
The paper relies on this without support.
The cell capture bias and lack of contextual microenvironment limits the power of scRNA-seq for predicting the origin and mechanisms of genetic diseases.
“However, the cell capture bias and lack of contextual microenvironment limits the power of this approach.”
The paper relies on this without support.
Dorsal cortical ventricular zones give rise to glutamatergic neurons that populate the cerebral cortex and hippocampus, and ventral ventricular zones of the lateral, medial, and caudal ganglionic eminence give rise to GABAergic interneurons that populate the striatum and globus pallidus and migrate into the cerebral cortex.
“Dorsal cortical ventricular zones give rise to glutamatergic neurons that populate the cerebral cortex and hippocampus. The ventral ventricular zones of the lateral, medial, and caudal ganglionic eminence give rise to GABAergic interneurons that populate the striatum and globus pallidus and migrate into the cerebral cortex.”
The paper relies on this without support.
The development of the dorsal midbrain has been less well characterized in terms of cellular taxonomy and organization compared with other brain regions.
“However, its development has been less well characterized in terms of cellular taxonomy and organization compared with other brain regions.”
The paper relies on this without support.
Topographic transcriptomic information is fundamental for dissection of the molecular events driving tissue patterning during development.
“Topographic transcriptomic information is fundamental for dissection of the molecular events driving tissue patterning during development.”
The paper relies on this without support.
Mice and humans differ in multiple aspects of normal physiology and disease.
“Mice and humans differ in multiple aspects of normal physiology and disease; hence, there are caveats”
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
“Importantly, conventional microscopy is compatible with these transcriptomic procedures, as demonstrated by Seq-Scope (hematoxylin and eosin (H&E) histology61) and Stereo-Seq (4,6-diamidino-2-phenylindole (DAPI) fluorescence62).”
From Spatial mapping of cellular senescence: emerging challenges and opportunities (2023) · cites this paper as [62]
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
“Previously, this technology has been utilized to create spatiotemporal maps of various life stages, including mouse brain, regenerating axolotl telencephalons, aging ovary, and brain.21”
From Spatial transcriptomic landscape unveils immunoglobin-associated senescence as a hallmark of aging (2024) · cites this paper as [21]
Experimental — accuracy not yet measuredThe cited source says what the claim attributes to it.
In the citing paper
“Recently developed spatial enhanced resolution omics-sequencing (Stereo-seq) utilized a randomly barcoded DNA nanoball patterned array chip to achieve single-cell resolution and has been employed to delineate the spatiotemporal transcriptomic landscape of mouse organ development [82].”
From Single-cell sequencing to multi-omics: technologies and applications (2024) · cites this paper as [82]
The cited source says what the claim attributes to it.
In the citing paper
“Recently, several ultra-high-resolution technologies such as Seq-Scope61, Stereo-seq62 and polony-indexed library sequencing (PIXEL-seq)63 have been developed, which could prove ideal for identification and characterization of senescent cells and their interactions with the environment. In addition to providing submicrometer resolution, which is comparable to that of optical microscopy, these meth”
From Spatial mapping of cellular senescence: emerging challenges and opportunities (2023) · cites this paper as [62]
The cited source says what the claim attributes to it.
In the citing paper
“Spatially resolved transcriptomics, which links cellular position to sequencing data, offers an in-depth view of gene expression within tissues.”
From Spatial transcriptomic landscape unveils immunoglobin-associated senescence as a hallmark of aging (2024) · cites this paper as [21]
In the citing paper
“However, current methods capture only one layer of omics information at a time, precluding the possibility of examining the mechanistic relationship across the central dogma of molecular biology.”
From Spatial epigenome–transcriptome co-profiling of mammalian tissues (2023) · cites this paper as [4]
In the citing paper
“Spatial epigenomics, transcriptomics and proteomics emerged recently1–5 but most of these can capture only one layer of the omics information.”
From Spatial epigenome–transcriptome co-profiling of mammalian tissues (2023) · cites this paper as [4]
In the citing paper
“We used an external ST data of an adult mouse brain from the MOSTA project.31”
From A spatial imaging-transcriptomics paradigm for deciphering the molecular basis of microscopic MRI in the normal brain and Alzheimer’s disease (2025) · cites this paper as [31]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
Each contradiction between two passages listed here was confirmed by an independent check.
Disease-gene count differs between Results and Methods
MinorThe Results and Figure 6A state that 1,959 disease-associated genes were analyzed, while the Methods state that filtering left 1,919 genes for further analysis.
“filtered them to select the top 1,959 genes based on the expression threshold in our dataset”
“After filtering out low-expressed genes with counts per million less than 1 in all anatomic regions, a list of total 1,919 genes were kept for further analysis.”
Figure legend labels an E15.1 section despite E15.5 sampling
MinorThe Figure 3A legend identifies an E15.1 section, but the paper describes one-day sampling intervals and lists E15.5, not E15.1, among the collected embryonic stages.
“Embryo sections including E9.5 E1S1, E10.5 E1S1, E11.5 E1S1, E12.5 E1S1, E13.5 E1S1, E14.5 E1S1, E15.1 E1S1, and E16.5 E1S1 are shown.”
“E9.5, E10.5, E11.5, E12.5, E13.5, E14.5, E15.5 and E16.5 embryos were collected from pregnant C57BL/6J female mice.”
Citations
Cites
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Self-citation
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Methods and applications for single-cell and spatial multi-omics
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An introduction to spatial transcriptomics for biomedical research
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Macrophage diversity in cancer revisited in the era of single-cell omics
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The expanding vistas of spatial transcriptomics
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Spatial profiling technologies illuminate the tumor microenvironment
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Spatial epigenome–transcriptome co-profiling of mammalian tissues
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Spatial biology of cancer evolution
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A single-cell time-lapse of mouse prenatal development from gastrula to birth
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An atlas of cells in the human tonsil
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Self-citationTranscriptomics in the era of long-read sequencing
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Self-citationSpatial Transcriptomics: Emerging Technologies in Tissue Gene Expression Profiling
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SODB facilitates comprehensive exploration of spatial omics data
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Self-citationWhole-brain spatial organization of hippocampal single-neuron projectomes
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Self-citationQuantitative characterization of cell niches in spatially resolved omics data
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Spatial multi-omics: novel tools to study the complexity of cardiovascular diseases
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Self-citationThe spatial landscape of gene expression isoforms in tissue sections
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Spatiotemporal transcriptomic maps of whole mouse embryos at the onset of organogenesis
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Self-citationBenchmarking clustering, alignment, and integration methods for spatial transcriptomics
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Self-citationSCS: cell segmentation for high-resolution spatial transcriptomics
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Self-citationScalable in situ single-cell profiling by electrophoretic capture of mRNA using EEL FISH
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Metadata
- Authors
- Chen, A., Liao, S., Cheng, M., Ma, K., Wu, L., Lai, Y., Qiu, X., Yang, J., Xu, J., Hao, S., Wang, X., Lu, H., Chen, X., Liu, X., Huang, X., Li, Z., Hong, Y., Jiang, Y., Peng, J., Liu, S., Shen, M., Liu, C., Li, Q., Yuan, Y., Wei, X., Zheng, H., Feng, W., Wang, Z., Liu, Y., Wang, Z., Yang, Y., Xiang, H., Han, L., Qin, B., Guo, P., Lai, G., Muñoz-Cánoves, P., Maxwell, P. H., Thiery, J. P., Wu, Q.-F., Zhao, F., Chen, B., Li, M., Dai, X., Wang, S., Kuang, H., Hui, J., Wang, L., Fei, J.-F., Wang, O., Wei, X., Lu, H., Wang, B., Liu, S., Gu, Y., Ni, M., Zhang, W., Mu, F., Yin, Y., Yang, H., Lisby, M., Cornall, R. J., Mulder, J., Uhlén, M., Esteban, M. A., Li, Y., Liu, L., Xu, X., & Wang, J.
- Affiliations
- BGI Group (China) (CN)
- Howard Hughes Medical Institute (US)
- MGI (CN)
- Chinese Academy of Sciences (CN)
- University of Cambridge (GB)
- Guangzhou Laboratory (CN)
- Guangdong Academy of Medical Sciences (CN)
- China National GeneBank (CN)
- University of Copenhagen (DK)
- University of Oxford (GB)
- Science for Life Laboratory (SE)
- Corresponding author
- Esteban, Miguel A.
- Li, Yuxiang
- Liu, Longqi
- Xu, Xun
- Wang, Jian
- ORCID
- Chen, Ao
- Liao, Sha
- Ma, Kailong
- Wu, Liang
- Lai, Yiwei
- Qiu, Xiaojie
- Xu, Jiangshan
- Hao, Shijie
- Wang, Xin
- Chen, Xi
- Liu, Xing
- Huang, Xin
- Jiang, Yujia
- Peng, Jian
- Liu, Chuanyu
- Li, Quanshui
- Yuan, Yue
- Wei, Xiaoyu
- Feng, Weimin
- Wang, Zhifeng
- Liu, Yang
- Wang, Zhaohui
- Yang, Yunzhi
- Xiang, Haitao
- Han, Lei
- Qin, Baoming
- Maxwell, Patrick H.
- Thiery, Jean Paul
- Chen, Bichao
- Li, Mei
- Wang, Shuai
- Wang, Liqun
- Fei, Ji-Feng
- Wang, Ou
- Wei, Xiaofeng
- Lu, Haorong
- Liu, Shiping
- Gu, Ying
- Ni, Ming
- Zhang, Wenwei
- Mu, Feng
- Yin, Ye
- Yang, Huanming
- Lisby, Michael
- Cornall, Richard J.
- Mulder, Jan
- Uhlén, Mathias
- Esteban, Miguel A.
- Li, Yuxiang
- Liu, Longqi
- Xu, Xun
- Wang, Jian
- Venue
- Cell
- Publisher
- Elsevier BV
- Published
- 2022-05
- Volume
- 185
- Issue
- 10
- Pages
- 1777-1792.e21
- ISSN
- 0092-8674 (ISSN-L)
- OpenAlex
- W4229033508
- Cited by
- 1,784
- References
- 54
- Citations by year
- 2026: 526 · 2025: 509 · 2024: 427 · 2023: 227 · 2022: 94
- FWCI
- 104.42
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Cell (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Research at the University of Copenhagen (University of Copenhagen) (publishedVersion, cc-by)
- Repositori digital de la UPF (Universitat Pompeu Fabra) (submittedVersion, cc-by)
- Funders
- Medical Research Council (MC_UU_00008/6)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Single-cell and spatial transcriptomics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Developmental Biology and Gene Regulation
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Genomics and Chromatin Dynamics
- Keywords
- spatiotemporal transcriptomics, Stereo-seq, spatial transcriptomics, embryonic development, dorsal midbrain, cell fate specification, transcriptional dynamics
- MeSH terms
- Animals, DNA, Embryo, Mammalian, Female, Mammals, Pregnancy, Sequence Analysis, RNA, Gene Expression Profiling, Organogenesis, Mice, Single-Cell Analysis, Transcriptome
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
A language model drew the claims from the paper on 2026-10-07, under version 3 of the claim definition (model deepseek-v4.1-flash, prompt extract-v3).
A second model labelled each claim on 2026-10-07 (model jev-1.13-20260917, definition revision gold-guideline-v0.2).
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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