Spatiotemporal transcriptomic atlas of mouse organogenesis using DNA nanoball-patterned arrays
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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.
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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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- Findingreasoning§ SUMMARY · p. 2
Stereo-seq was created by combining DNA nanoball-patterned arrays and in situ RNA capture.
“Here, we combined DNA nanoball (DNB)-patterned arrays and in situ RNA capture to create spatial enhanced resolution omics-sequencing (Stereo-seq).”
Quote in the paperSubstantial claim - Findingnone given§ Highlights · p. 1
Stereo-seq maps the spatiotemporal transcriptomic dynamics during mouse organogenesis.
“Stereo-seq maps the spatiotemporal transcriptomic dynamics during mouse organogenesis”
Quote in the paperSubstantial claim - Findingown data§ Figure 3. Spatiotemporal transcriptomic atlas of mouse organogenesis · p. 9
In the E16.5 embryo, the localization of regulons across serial sections of the same organ could be defined, such as GATA4 and TBX5 in the ventriculus and atrium of the developing heart, respectively.
“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).”
Quote in the paperSubstantial claim - Findingown data§ Spatially resolved transcriptomic atlas of mouse organogenesis · p. 7
Reconstruction of developmental trajectories for annotated regions in whole embryos using TOME showed spatiotemporal connections largely consistent with contemporary understanding of mouse development.
“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).”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites Qiu et al., 2022a
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- Findingown data§ Spatially resolved transcriptomic atlas of mouse organogenesis · p. 7
Expression of many known marker genes in these brain clusters is consistent with known cellular distributions across the corresponding anatomic regions.
“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).”
Quote in the paperSubstantial claim
- Findingown data§ Spatially resolved transcriptomic atlas of mouse organogenesis · p. 7
The external boundaries of these clusters closely resembled the anatomic regions.
“Remarkably, the external boundaries of these clusters closely resembled the anatomic regions (Figures 3A and S2C and MOSTA website).”
Quote in the paperSubstantial claim - Findingown data§ DNB-patterned arrays enable large field-of-view spatially resolved transcriptomics with high definition · p. 3
Stereo-seq captured UMI counts ranging on average from 69 per 2 μm bin, 1,450 per 10 μm bin, and 133,776 per 100 μm bin.
“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).”
Quote in the paperSubstantial claim - Findingown data§ Spatially resolved transcriptomic atlas of mouse organogenesis · p. 7
Tissue-specific identities were confirmed by visualizing specific marker genes on spatial maps across all embryonic time points.
“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).”
Quote in the paperSubstantial claim - Findingown data§ DNB-patterned arrays enable large field-of-view spatially resolved transcriptomics with high definition · p. 3
The distribution of genes and UMI per bin was highly consistent between adjacent sections (R2 = 0.963).
“The distribution of genes and UMI per bin was highly consistent between adjacent sections (R 2 = 0.963) (Figures S1B and S1C).”
Quote in the paperNot a substantial claim - Findingown data§ Spatially resolved transcriptomic atlas of mouse organogenesis · p. 7
The extended coverage of the E16.5 embryo allowed serial images in developing tissues and organs, such as the meninges (Atp1a2+), spinal cord (Hoxb8+), and lung (Sftpc+).
“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).”
Quote in the paperNot a substantial claim - Findingown data§ A · p. 4
Segmented cells displayed differential spatial distribution between nucleus-localized transcripts (Malat1 and Neat1) and cytoplasmic-enriched mitochondrial transcripts within each cell, consistent with the subcellular resolution of Stereo-seq.
“The segmented cells displayed differential spatial distribution between nucleus-localized transcripts ( Malat1 and Neat1 ) and cytoplasmic-enriched mitochondrial transcripts within each cell, consistent with the subcellular resolution of Stereo-seq (Figure S1G).”
Quote in the paperNot a substantial claim - Findingnone given§ A · p. 5
The location of the different cell types was consistent with existing knowledge of the corresponding anatomic regions annotated using bins.
“Moreover, the location of the different cell types was consistent with existing knowledge of the corresponding anatomic regions annotated using bins.”
Quote in the paperSubstantial claim - Findingown data§ DNB-patterned arrays enable large field-of-view spatially resolved transcriptomics with high definition · p. 3
Stereo-seq is superior to other reported technologies including SeqScope (848 UMI on average per 10 μm diameter bin) when compared at the same resolution.
“This is superior to other reported technologies including SeqScope (848 UMI on average per 10 m m diameter bin) when compared at the same resolution.”
Quote in the paperSubstantial claim - Findingown data§ Spatial heterogeneity of cell types in mouse embryonic tissues · p. 9
Reclustering of the chondrocyte population identified distinct cell states including round, columnar, prehypertrophic, early hypertrophic, and late hypertrophic chondrocytes, as well as nucleus pulposus cells.
“Reclustering of the chondrocyte population identified distinct cell states including round ( Sox5 + , Sox9 + ), columnar ( Sulf1 + , Robo1 + ), prehypertrophic ( Prrx1 + , Fbxl7 + ), early hypertrophic ( Runx2 + , Ibsp + ), and late hypertrophic ( Spp1 + , Mmp9 + ) chondrocytes, as well as the nucleus pulposus cells ( Pax1 + , Fmod + ) (Figures 4C, S5F, and S5G).”
Quote in the paperSubstantial claim - Findingown data§ Spatially resolved transcriptomic atlas of mouse organogenesis · p. 7
In total, we retrieved transcriptomic information for 3,511,403 bins.
“In total, we retrieved transcriptomic informationfor3,511,403bins.”
Quote in the paperSubstantial claim - Findingown data§ Spatially resolved transcriptomic atlas of mouse organogenesis · p. 7
Projection of publicly available scRNA-seq data of the developing brain onto the reclustered E9.5 brain using Tangram identified the localization of progenitor cell types including secondary organizers.
“Projection of publicly available scRNA-seq data (La Manno et al., 2021) of the developing brain onto the reclustered E9.5 brain using Tangram (Biancalani et al., 2021) identified the localization of progenitor cell types including secondary organizers (e.g., midbrain basal plate, midbrain floor plate, ventricle roof plate, and hindbrain floor plate) (Figure S3E), which are specialized substruct”
Quote in the paperNot a substantial claim0 of 2 sources checked - Findingown data§ Spatially resolved transcriptomic atlas of mouse organogenesis · p. 7
Unsupervised SCC of these bins showed transcriptomic configurations matching the localization of major tissues and organs at each time point.
“Unsupervised SCC of these bins showed transcriptomic configurations matching the localization of major tissues and organs (e.g., skin, bone, muscle nervous system, thymus, heart, lung, liver, pancreas, kidney, adrenal gland, gastrointestinal tract, genitourinary tract, and ovary) at each time point.”
Quote in the paperSubstantial claim - Findingown data§ Spatially resolved transcriptomic atlas of mouse organogenesis · p. 7
Reclustering of E9.5, E12.5, E13.5, E14.5, E15.5, and E16.5 brains produced clusters that matched well with anatomically defined brain regions including ventricular and mantle zones of the pallium, subpallium, midbrain, hindbrain, diencephalon, cerebellum, hypothalamus, olfactory bulb, and choroid plexus.
“We also reclustered the E9.5, E12.5,E13.5,E14.5,E15.5,andE16.5brains(Figure3C).Thesubsequentclusters matched well with anatomically defined brain regions including the ventricular and mantle zones of the pallium, subpallium, midbrain, hindbrain, diencephalon, cerebellum, hypothalamus, olfactory bulb, and choroid plexus.”
Quote in the paperSubstantial claim - Findingown data§ Spatially resolved transcriptomic atlas of mouse organogenesis · p. 7
We profiled 53 sagittal sections from C57BL/6 mouse embryos spanning E9.5-E16.5 with one-day intervals.
“We profiled 53 sagittal sections from C57BL/6 mouse embryos spanning E9.5-E16.5 with one-day intervals.”
Quote in the paperSubstantial claim - Findingown data§ Figure 3. Spatiotemporal transcriptomic atlas of mouse organogenesis · p. 9
Application of SCENIC to binned Stereo-seq maps and Hotspot identified multiple regulon modules associated with regional specificity at each stage.
“This identified multiple regulon modules associated with regional specificity at each stage.”
Quote in the paperSubstantial claim0 of 1 sources checked- cites DeTomaso and Yosef, 2021 source
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- Findingown data§ Spatial heterogeneity of cell types in mouse embryonic tissues · p. 9
Clustering of segmented cells in different anatomic regions of the E16.5 brain identified progenitor cells, neurons, and non-neuronal cell types based on marker gene expression.
“Clustering of the segmented cells in different anatomic regions identified different types of progenitor cells (e.g., radial glia cells, neuroblasts, and glioblasts), neurons (e.g., GABAergic interneurons, excitatory glutamatergic neurons, and dopaminergic neurons), and non-neuronal cell types (e.g., endothelial cells and fibroblasts) based on the expression of marker genes (Figures S6A and MOSTA”
Quote in the paperSubstantial claim- Rests on an assumption: The precise regulation of cell identity in the developing brain is critical for the establishment of brain architecture but is yet poorly understood.
- Findingown data§ A · p. 4
Unsupervised SCC of binned Stereo-seq data identified different anatomic regions of the adult mouse coronal hemibrain, including cortex and subcortical regions such as hippocampus, thalamus, and striatum.
“To analyze the brain data, we performed unsupervised SCC of binned (bin 50, 50 3 50 DNB bins, 25 m m diameter) Stereo-seq data to identify the different anatomic regions. These included the cortex and subcortical regions such as hippocampus, thalamus, and striatum, among others (Figure 2C).”
Quote in the paperSubstantial claim - Findingreasoning§ DNB-patterned arrays enable large field-of-view spatially resolved transcriptomics with high definition · p. 3
A spatially constrained-clustering (SCC) algorithm optimized for analysis of larger, more complex tissues was developed.
“Considering the specific Stereo-seq features, we developed a spatially constrained-clustering (SCC) algorithm optimized for analysis of larger, more complex tissues (see STAR Methods).”
Quote in the paperSubstantial claim - Findingown data§ Spatially resolved transcriptomic atlas of mouse organogenesis · p. 7
GO enrichment analysis demonstrated that most spatially restricted gene programs correspond to features related to organ or region-specific biological processes.
“Likewise, gene ontology (GO) enrichment analysis demonstrated that most of these spatially restricted geneprogramscorrespondtofeaturesrelatedtoorganor region-specific biological processes (Figures S4A and S4B).”
Quote in the paperNot a substantial claim - Findingnone given§ DNB-patterned arrays enable large field-of-view spatially resolved transcriptomics with high definition · p. 3
The use of random barcode-labeled DNB achieves a large spatial barcode pool size (4^25 distinct spots).
“The use of random barcode-labeled DNB achieves a large spatial barcode pool size (4 25 distinct spots).”
Quote in the paperSubstantial claim - Findingown data§ Spatial heterogeneity of cell types in mouse embryonic tissues · p. 9
Unsupervised clustering of the gene-by-cell matrix and UMAP visualization revealed 25 major cell types based on known markers.
“Unsupervised clustering of the gene-by-cell matrix and uniform manifold approximation and projection (UMAP) visualization revealed 25 major cell types based on known markers (Figures 4A and S5B and MOSTA website).”
Quote in the paperSubstantial claim - Findingnone given§ Highlights · p. 1
Stereo-seq enables large field-of-view spatial transcriptomics at cellular resolution.
“Stereo-seq enables large field-of-view spatial transcriptomics at cellular resolution”
Quote in the paperSubstantial claim - Findingnone given§ Highlights · p. 1
Stereo-seq reveals the spatial cell-type heterogeneity of mouse embryonic tissues.
“Stereo-seq reveals the spatial cell-type heterogeneity of mouse embryonic tissues”
Quote in the paperSubstantial claim - Findingown data§ DNB-patterned arrays enable large field-of-view spatially resolved transcriptomics with high definition · p. 3
These capture areas are substantially larger than those achieved by other reported technologies.
“These capture areas are substantially larger than those achieved by other reported technologies (Figure 1B)”
Quote in the paperSubstantial claim - Findingown data§ Spatial heterogeneity of cell types in mouse embryonic tissues · p. 9
The developing bone showed gradual appearance and maturation of chondrocytes at different anatomic regions.
“Similarly, the developing bone showed gradual appearance and maturation of chondrocytes at different anatomic regions.”
Quote in the paperNot a substantial claim - Findingown data§ Spatial heterogeneity of cell types in mouse embryonic tissues · p. 10
SCENIC analysis identified enrichment for transcription factors with specific activities associated with maturation of projection neurons and interneurons (PAX6 in apical progenitors, MEF2C in cortical projection neurons, and DLX1 in interneurons).
“This identified enrichment for transcription factors with specific activities associated with the maturation of projection neurons and interneurons (i.e., PAX6 in apical progenitors, MEF2C in cortical projection neurons, and DLX1 in the interneurons) (Di Bella et al., 2021; Lim et al., 2018) (Figure S6H).”
Quote in the paperNot a substantial claim0 of 2 sources checked - Findingown data§ Deciphering developmental disease susceptibility using MOSTA · p. 12
Cell-resolution reclustering of gene-by-cell expression matrices from the maxilla and limb of the E16.5 embryo identified different cell types including mesenchymal cells, fibroblasts, endothelial cells, keratinocytes, tenocytes, myoblasts, chondrocytes, and others.
“This identified different cell types including mesenchymal cells, fibroblasts, endothelial cells, keratinocytes, tenocytes, myoblasts, chondrocytes, and others.”
Quote in the paperSubstantial claim - Findingown data§ Spatial heterogeneity of progenitor cells in the developing dorsal midbrain · p. 10
Reclustering of the gene-by-cell expression matrix from the developing dorsal midbrain at E12.5, E14.5, and E16.5 revealed multiple cell types including terminally differentiated cells and progenitors based on marker gene expression.
“Reclustering of the gene-by-cell expression matrix revealed multiple cell types including terminally differentiated cells (e.g., GABAergic neurons, glutamatergic neurons, microglia, fibroblasts, and endothelial cells) and progenitors (radial glia cells, neuroblasts, and glioblasts) based on marker gene expression (Figures 5A, S7A, and S7B).”
Quote in the paperSubstantial claim - Findingcitation§ Spatial heterogeneity of cell types in mouse embryonic tissues · p. 10
Vector field-based pseudotime analysis showed a trajectory toward mature interneurons, indicated by a shift from early progenitor markers Epha5, Tcf4, and Erbb4 to the late maturation marker Gad2.
“This showed the trajectory toward mature interneurons as indicated by the shift from the early progenitor markers Epha5 , Tcf4 , and Erbb4 to the late maturation marker Gad2 (Lim et al., 2018).”
Quote in the paperSubstantial claim0 of 1 sources checked- cites Lim et al., 2018 source
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- Findingown data§ Spatial heterogeneity of progenitor cells in the developing dorsal midbrain · p. 10
Cell-cycle score analysis confirmed that radial glia cells show higher proliferation compared with neuroblasts and glioblasts.
“Cell-cycle score analysis confirmed that radial glia cells show higher proliferation compared with neuroblasts and glioblasts.”
Quote in the paperNot a substantial claim - Findingown data§ Spatial heterogeneity of progenitor cells in the developing dorsal midbrain · p. 10
Variations in the pattern of progenitor cell appearance and maturation were observed across the different embryonic stages.
“Interestingly, we noticed variations in the pattern of progenitor cell appearance and maturation across the different embryonic stages (Figures 5B, 5C, S7C, and S7D).”
Quote in the paperNot a substantial claim - Findingown data§ Spatial heterogeneity of progenitor cells in the developing dorsal midbrain · p. 10
Regulon analysis of the E12.5-E16.5 developing dorsal midbrain identified putative transcription factors associated with neuroblast and glioblast lineage commitment.
“Likewise, regulon analysis of the E12.5-E16.5 developing dorsal midbrain identified putative transcription factors associated with neuroblast and glioblast lineage commitment (Figures 5J and 5K).”
Quote in the paperSubstantial claim - Findingown data§ Spatial heterogeneity of progenitor cells in the developing dorsal midbrain · p. 10
Both neuroblasts and glioblasts were not evenly distributed along the ventricular zone but organized in glioblast- and neuroblast-rich patches.
“Intriguingly, both neuroblasts and glioblasts were not evenly distributed along the ventricular zone but organized in glioblastand neuroblast-rich patches.”
Quote in the paperSubstantial claim - Findingown data§ DISCUSSION · p. 14
Using spatial RNA velocity, the study generated transcriptomic maps of tangentially migratory interneurons in the developing cerebral cortex.
“Using spatial RNA velocity, we have generated transcriptomic maps of tangentially migratory interneurons in the developing cerebral cortex.”
Quote in the paperSubstantial claim - Findingown data§ Spatial heterogeneity of cell types in mouse embryonic tissues · p. 10
Spatial RNA velocity analysis revealed strong directional flows consistent with tangential migration of interneurons from the MGE into the cerebral cortex.
“Spatial RNA velocity analysis revealed strong directional flows consistent with the tangential migration of interneurons from the MGE into the cerebral cortex (Figure 4E).”
Quote in the paperSubstantial claim - Findingown data§ DISCUSSION · p. 14
The analysis identified tissue- and area-specific transcription factors and their associated targets across different embryonic stages.
“This identified tissue- and area-specific transcription factors and their associated targets across different embryonic stages.”
Quote in the paperSubstantial claim - Findingown data§ Deciphering developmental disease susceptibility using MOSTA · p. 12
Projection of 1,959 DDG2P genes onto spatial transcriptomic maps showed enrichment in different tissues and organs including brain, spinal cord, heart, liver, lung, connective tissue, bone, and muscle.
“This showed enrichment in different tissues and organs including brain, spinal cord, heart, liver, lung, connective tissue, bone, and muscle (Figure 6A).”
Quote in the paperSubstantial claim - Findingown data§ DISCUSSION · p. 14
Examples include the identification of neuroblast-rich and glioblast-rich domains within the ventricular zone of the developing dorsal midbrain, which will give rise to specific patterning consisting of many cell subtypes.
“Examples of the latter include the identification of neuroblast-rich and glioblast-rich domains within the ventricular zone of the developing dorsal midbrain, which will give rise to the specific patterning consisting of many cell subtypes.”
Quote in the paperNot a substantial claim - Findingcitation§ Figure S1. Performance of stereo-seq, related to Figures 1 and 2 · p. 26
There is correspondence between the indicated cell types identified by Stereo-seq and the cell clusters identified by reported scRNA-seq (Zeisel et al., 2018).
“(K) Correspondence between the indicated cell types identified by Stereo-seq and the cell clusters identified by reported scRNA-seq (Zeisel et al., 2018).”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites Zeisel et al., 2018 source
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- Findingcitation§ Figure S1. Performance of stereo-seq, related to Figures 1 and 2 · p. 26
There is correspondence between the indicated cell subtypes identified by Stereo-seq and the cell subtypes identified by reported scRNA-seq (Zeisel et al., 2018).
“(L) Correspondence between the indicated cell subtypes identified by Stereo-seq and the cell subtypes identified by reported scRNA-seq (Zeisel et al., 2018).”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites Zeisel et al., 2018 source
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- Findingown data§ Figure S1. Performance of stereo-seq, related to Figures 1 and 2 · p. 26
The Pearson correlation coefficient (R2) of pseudo-bulk profiles from the two mouse olfactory bulb sections was 0.9666.
“(C) Pearson correlation coefficient (R 2 = 0.9666) of pseudo-bulk profiles from the two mouse olfactory bulb sections.”
Quote in the paperNot a substantial claim - Premisecitation§ SUMMARY · p. 2
Current spatially resolved transcriptomic methodologies have an imbalance between resolution, gene capture, and field of view that precludes their systematic application to relatively large and three-dimensional mid- and late-gestation embryos.
“However, the imbalance between resolution, gene capture, and field of view of currentmethodologiesprecludestheirsystematicapplicationtoanalyzerelativelylargeandthree-dimensional mid- and late-gestation embryos.”
Quote in the paperSubstantial claim0 of 1 sources checked- cites Rao et al., 2021 source
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- Study design and conductown data§ Spatial heterogeneity of cell types in mouse embryonic tissues · p. 10
Alignment of Stereo-seq data with a scRNA-seq study of the developing mouse cortex using Tangram confirmed the reported spatial profiles of these cell types.
“We also aligned the Stereo-seq data with a scRNA-seq study from the developing mouse cortex (Di Bella et al., 2021) using Tangram, confirming the reported spatial profiles of these cell types (Figure S6C).”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites Di Bella et al., 2021 source
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- Study design and conductown data§ Spatial heterogeneity of cell types in mouse embryonic tissues · p. 9
Alignment of Stereo-seq data with scRNA-seq data using Tangram confirmed the reported probabilistic spatial profiles for specific cell types in the whole brain.
“To validate our results, we aligned with scRNA-seq data using Tangram (Biancalani et al., 2021), which confirmed the reported probabilistic spatial profile for specific cell types such as choroid plexus, hindbrain glioblasts, forebrain GABAergic neuroblasts, midbrain glutamatergic neuroblasts, cortical and hippocampal glutamatergic neurons, and forebrain GABAergic neurons in the whole brain (La M”
Quote in the paperSubstantial claim0 of 2 sources checked - Premisereasoning§ INTRODUCTION · p. 3
No available technology can profile whole mouse embryos in mid- or late-stage gestation in an unbiased manner and with high definition (single-cell resolution and high sensitivity).
“However, no available technology can profile whole mouse embryos in mid- or late-stage gestation in an unbiased manner and with high definition (single-cell resolution and high sensitivity).”
Quote in the paperSubstantial claim0 of 1 sources checked- cites Rao et al., 2021 source
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- Premisecitation§ INTRODUCTION · p. 3
Several recently developed spatially resolved transcriptomic methodologies share a major caveat in limited field of view.
“These techniques all have specific advantages and disadvantages in resolution and applicability but share a major caveat in the limited field of view.”
Quote in the paperSubstantial claim0 of 1 sources checked- cites Rao et al., 2021 source
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- Premisecitation§ DNB-patterned arrays enable large field-of-view spatially resolved transcriptomics with high definition · p. 3
DNB sequencing is based on lithographically etched chips (patterned arrays) for in situ sequencing.
“DNB sequencing is based on lithographically etched chips (patterned arrays) for in situ sequencing (Drmanac et al., 2010).”
Quote in the paperSubstantial claim0 of 1 sources checked- cites Drmanac et al., 2010 source
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- Interpretationown data§ Figure 3. Spatiotemporal transcriptomic atlas of mouse organogenesis · p. 9
MOSTA generated by Stereo-seq constitutes a vast and unbiased topographic transcriptomic resource to investigate the molecular basis of tissue patterning during mouse organogenesis.
“MOSTA generated by Stereo-seq constitutes a vast and unbiased topographic transcriptomic resource to investigate the molecular basis of tissue patterning during mouse organogenesis”
Quote in the paperSubstantial claim - Study design and conductcitation§ A · p. 5
The validity of the cell-type annotations was confirmed by comparison with a reported scRNA-seq dataset for the concordant spatial domains.
“The validity of these annotations was confirmed by comparison with a reported scRNA-seq dataset for the concordant spatial domains (Zeisel et al., 2018) (Figures S1K and S1L).”
Quote in the paperSubstantial claim0 of 1 sources checked- cites Zeisel et al., 2018 source
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- Premisecitation§ Spatially resolved transcriptomic atlas of mouse organogenesis · p. 7
The stages E9.5-E16.5 include most of the key events in mouse organogenesis.
“These stages include most of the key events in mouse organogenesis (Cao et al., 2019).”
Quote in the paperSubstantial claim0 of 1 sources checked- cites Cao et al., 2019 source
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- Study design and conductcitation§ Spatial heterogeneity of cell types in mouse embryonic tissues · p. 10
Reanalysis of a reported scATAC-seq dataset of the developing cortex showed consistency between the top transcription factor hits identified with Stereo-seq and the DNA-binding motif deviation score in the corresponding cell types.
“Reanalysis of a reported scATAC-seq dataset of the developing cortex (Di Bella et al., 2021) showed consistency between the top transcription factor hits identified with Stereo-seq and the DNA-binding motif deviation score in the corresponding cell types (Figure S6I).”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites Di Bella et al., 2021 source
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- Interpretationown data§ DISCUSSION · p. 14
Projection of developmental disease loci onto spatial maps highlighted a potentially relevant functional connection between Wnt5 and Msx1 in different cell types, especially in mesenchymal cells in Robinow syndrome.
“In addition, we have projected the expression of developmental disease loci (including monogenic diseases involving transcription factors or morphogens) onto our spatial maps, highlighting a potentially relevant functional connection between Wnt5 and Msx1 in different cell types but especially in mesenchymal cells in Robinow syndrome.”
Quote in the paperNot a substantial claim - Study design and conductcitation§ Spatial heterogeneity of progenitor cells in the developing dorsal midbrain · p. 10
The expression patterns of Hes1 and Hes5 are consistent with ISH images from the ABA.
“The expression patterns of Hes1 and Hes5 are consistent with ISH images taken from ABA (Figure S7H) (Lein et al., 2007).”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites Lein et al., 2007 source
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- Interpretationown data§ Spatial heterogeneity of progenitor cells in the developing dorsal midbrain · p. 10
The patchy distribution indicates a programmed organization of units along the ventricular zone that give rise to different cell types populating the neuron-rich and glia-rich regions of the superior colliculus.
“This indicates the programmed organization of units along the ventricular zone that give rise to different cell types that populate the neuron-rich and glia-rich regions of the superior colliculus.”
Quote in the paperSubstantial claim - Interpretationnone given§ DISCUSSION · p. 14
The study used Stereo-seq to create MOSTA, an expandable panoramic and high-definition transcriptomic resource for understanding mouse organogenesis.
“We have used Stereo-seq to create MOSTA, an expandable panoramic and high-definition transcriptomic resource for understanding mouse organogenesis.”
Quote in the paperSubstantial claim - Premisecitation§ Deciphering developmental disease susceptibility using MOSTA · p. 12
Many genetic conditions that present later have a developmental, often unnoticed, component because their target genes are also expressed during organogenesis.
“However, many genetic conditions that present later have a developmental, often unnoticed, component because their target genes are also expressed during organogenesis (Boycott and Ardigo, 2018; Barnat et al., 2020).”
Quote in the paperSubstantial claim0 of 2 sources checked- cites Boycott and Ardigo, 2018
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- cites Barnat et al., 2020 source
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- Study design and conductcitation§ Spatial heterogeneity of cell types in mouse embryonic tissues · p. 10
The expression patterns of Erbb4 and Gad2 are consistent with ISH images from the ABA.
“The expression patterns of Erbb4 and Gad2 are consistent with ISH images taken from ABA (Lein et al., 2007) (Figure S6F).”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites Lein et al., 2007 source
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- Interpretationown data§ DISCUSSION · p. 14
MOSTA provides detailed topographic information about the stepwise emergence of tissue-specific cell identities across different stages of mouse organogenesis.
“MOSTA provides a detailed topographic information about the stepwise emergence of tissue-specific cell identities across the different stages of mouse organogenesis.”
Quote in the paperSubstantial claim - Study design and conductown data§ Spatial heterogeneity of progenitor cells in the developing dorsal midbrain · p. 10
Spatial pseudotime analysis of progenitor cells at the three stages using monocle 3 confirmed the results.
“Spatial pseudotime analysis of progenitor cells in these three stages using monocle 3 (Cao et al., 2019) confirmed these results (Figures 5D-5F and S7E).”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites Cao et al., 2019 source
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- Interpretationown data§ Spatial heterogeneity of progenitor cells in the developing dorsal midbrain · p. 12
The study provides a comprehensive spatially resolved transcriptomic map of progenitor cell specification in the dorsal midbrain.
“We, thus, provide a comprehensive spatialy resolved transcriptomic map of progenitor cell specification in the dorsal midbrain.”
Quote in the paperSubstantial claim - Study design and conductcitation§ Spatial heterogeneity of progenitor cells in the developing dorsal midbrain · p. 10
The results are in line with ISH images of Rspo1 and Fgf15 from the ABA.
“These results are in line with ISH images of the Rspo1 and Fgf15 taken from ABA (Lein et al., 2007) (Figure S7F).”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites Lein et al., 2007 source
Not checked in the free run.
- Interpretationown data§ DISCUSSION · p. 14
To illustrate the utility of MOSTA for inferring regulatory mechanisms behind developmental processes, the study systematically dissected spatially resolved gene regulons driving organogenesis.
“To illustrate the utility of MOSTA for inferring the regulatory mechanisms behind developmental processes, we have systematically dissected the spatially resolved gene regulons driving organogenesis.”
Quote in the paperSubstantial claim - Interpretationown data§ Deciphering developmental disease susceptibility using MOSTA · p. 12
The differential expression of Wnt5a suggests that disease mechanisms may differ in the maxilla and limb tissues.
“This suggests that the disease mechanisms may differ in the two tissues.”
Quote in the paperSubstantial claim - Interpretationreasoning§ Limitations of the study · p. 14
The incomplete sampling could affect the inference of relationships between anatomic regions and developmental trajectories.
“The incomplete sampling could also affect the inference of relationship of anatomic regions and developmental trajectories.”
Quote in the paperNot a substantial claim - Interpretationreasoning§ Limitations of the study · p. 14
The limitation of missing low-expression genes is mitigated because the large field-of-view facilitates profiling of enough cell numbers to power statistical analysis.
“This is mitigated because the large field-of-view facilitates the profiling of enough cell numbers to power the statistical analysis.”
Quote in the paperNot a substantial claim
- May combine two claims. 51
- The quote only partly states this claim. 2
- Classified as probably not a substantial claim. 33
May combine two claims. The quote only partly states this claim.
Findingown data§ Spatial heterogeneity of cell types in mouse embryonic tissues · p. 9The epithelial cell cluster (Epcam+, Krt8+) could be grouped into specialized epithelial cell subtypes including hair follicle cells, alveolar type II cells, club cells, ciliated cells, zona glomerulosa epithelial cells, glandular epithelium, stratified squamous epithelium, renal tubular cells, and thymic epithelial cells.
“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”
Quote in the paperNot a substantial claimMay combine two claims.
Findingown data§ A · p. 4There was substantial colocalization of nucleic acid dye and aggregated transcripts detected by Stereo-seq, illustrating that cells can be segmented in an image-dependent manner.
“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.”
Quote in the paperSubstantial claimMay combine two claims.
Findingown data§ A · p. 4Image-based cell segmentation yielded 50,140 segmented cells with an average of 1,910 UMI and 792 genes per cell.
“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).”
Quote in the paperSubstantial claimMay combine two claims.
Findingown data§ Spatially resolved transcriptomic atlas of mouse organogenesis · p. 7For 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.
“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).”
Quote in the paperSubstantial claimMay combine two claims.
Findingown data§ Figure 3. Spatiotemporal transcriptomic atlas of mouse organogenesis · p. 9Among 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.
“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).”
Quote in the paperSubstantial claimMay combine two claims.
Findingown data§ DNB-patterned arrays enable large field-of-view spatially resolved transcriptomics with high definition · p. 3Stereo-seq has a larger number of spots per 100 μm2, with smaller spot size and center-to-center distance, than any other published method.
“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).”
Quote in the paperSubstantial claimMay combine two claims.
Findingown data§ Figure 3. Spatiotemporal transcriptomic atlas of mouse organogenesis · p. 9At E9.5, 31 modules containing a total of 498 regulons were identified, and at E16.5, 35 modules containing a total of 469 regulons that correspond to different tissues.
“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).”
Quote in the paperSubstantial claimMay combine two claims.
Findingown data§ Spatially resolved transcriptomic atlas of mouse organogenesis · p. 7In the E13.5 spinal cord, Stereo-seq 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, it localized artery (Myl9+), cortex (Lhx1+), medulla (Nrxn3+), capsule (Robo2+), and glomeruli (Poxdl+).
“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”
Quote in the paperSubstantial claimMay combine two claims.
Findingnone given§ INTRODUCTION · p. 3Stereo-seq combines single-cell resolution, high sensitivity, and a large field of view.
“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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Findingown data§ Spatially resolved transcriptomic atlas of mouse organogenesis · p. 7The average number of captured genes 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.
“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).”
Quote in the paperNot a substantial claimThe quote only partly states this claim.
Findingown data§ Spatially resolved transcriptomic atlas of mouse organogenesis · p. 7Spatial reclustering of bins from areas corresponding to selected tissues revealed detailed subregions, including in E13.5 spinal cord and E16.5 kidney.
“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.”
Quote in the paperSubstantial claimProbably not a substantial claim: may not pass S2 (checkable), S3 (load-bearing).
Findingown data§ Spatial heterogeneity of cell types in mouse embryonic tissues · p. 9Reclustering of specific cell populations could further identify cell subtypes or cell states.
“Reclustering of specific cell populations could further identify cell subtypes or cell states.”
Quote in the paperNot a substantial claimMay combine two claims.
Findingown data§ Spatial heterogeneity of cell types in mouse embryonic tissues · p. 9After filtering out low-capture cells, the E16.5 embryo section yielded 281,377 segmented cells with an average of 1107 UMI and 529 genes per cell.
“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).”
Quote in the paperSubstantial claimMay combine two claims.
Findingown data§ Spatial heterogeneity of cell types in mouse embryonic tissues · p. 9The 25 major cell types were either located in specific anatomic regions or represented common cell types spread across multiple sites.
“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).”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S3 (load-bearing).
Findingown data§ Spatially resolved transcriptomic atlas of mouse organogenesis · p. 7The distribution of genes and UMI per bin varied between sections from different time points.
“Thedistribution of genes and UMI perbin varied between sections from different time points (Figure S2B).”
Quote in the paperNot a substantial claimMay combine two claims.
Findingown data§ DNB-patterned arrays enable large field-of-view spatially resolved transcriptomics with high definition · p. 3Stereo-seq chips of up to an effective area of 13.2 cm × 13.2 cm have been used for different tissue sizes, subdivided into 50, 100, and 200 mm2 chips for profiling sections from mouse olfactory bulb, mouse hemibrain, and whole mouse embryos.
“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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Findingown data§ DNB-patterned arrays enable large field-of-view spatially resolved transcriptomics with high definition · p. 3The distribution of specific markers (Pcp4 and Slc17a7) captured by Stereo-seq exhibited the expected similarity to in situ hybridization data from Allen Brain Atlas and clearer patterns than HDST or Slide-seqV2-resolved expression data.
“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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Findingown data§ Spatially resolved transcriptomic atlas of mouse organogenesis · p. 7Gene programs identified by Hotspot showed well-defined spatial patterns that largely match anatomic regions and contained sets of genes that could be validated by ISH.
“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).”
Quote in the paperSubstantial claim0 of 1 sources checked- cites DeTomaso and Yosef, 2021
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- Rests on an assumption: Amalgamation of different signals in complex tissues can complicate interpretations.
May combine two claims.
Findingreasoning§ DNB-patterned arrays enable large field-of-view spatially resolved transcriptomics with high definition · p. 3Standard DNB chips have spots with approximately 220 nm diameter and a center-to-center distance of 500 or 715 nm, providing up to 400 spots for tissue RNA capture per 100 μm2.
“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 .”
Quote in the paperSubstantial claimMay combine two claims.
Findingown data§ Deciphering developmental disease susceptibility using MOSTA · p. 12High Wnt5a expression was observed in mesenchymal cells and fibroblasts from the maxilla, whereas in the limb it was located mainly in mesenchymal cells.
“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).”
Quote in the paperSubstantial claimMay combine two claims.
Findingown data§ Spatial heterogeneity of progenitor cells in the developing dorsal midbrain · p. 10HES6 was highly active in neuroblasts, and its target genes included Ascl1 and Hes5.
“HES6 was highly active in neuroblasts, and its target genes included Ascl1 and Hes5 (Figures 5L and S7G).”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.
Findingown data§ Spatial heterogeneity of cell types in mouse embryonic tissues · p. 10Unknown potential regulators Ttc3 and Celf4 of interneuron migration were observed and could be interesting for further exploration.
“We also observed unknown potential regulators ( Ttc3 and Celf4 ) of interneuron migration that could be interesting for further exploration (Figure 4F).”
Quote in the paperNot a substantial claimMay combine two claims.
Findingown data§ Spatial heterogeneity of progenitor cells in the developing dorsal midbrain · p. 10Highly specific expressions of extrinsic cues (ligands) were identified in glioblasts (e.g., Rspo1/2/3, Wnt1, Wnt9a) and neuroblasts (Fgf15, Wnt7b, Tnc), providing potential mechanistic insight for further specification of these progenitors.
“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.”
Quote in the paperSubstantial claimProbably not a substantial claim: may not pass S3 (load-bearing).
Findingown data§ DISCUSSION · p. 14Stereo-seq is an affordable technology.
“Moreover, Stereo-seq is an affordable technology.”
Quote in the paperNot a substantial claimMay combine two claims.
Findingown data§ Spatial heterogeneity of progenitor cells in the developing dorsal midbrain · p. 10At E14.5, radial glia cells disappeared from the rostral part and remained in the more caudal domains of the ventricular zone.
“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.”
Quote in the paperSubstantial claimMay combine two claims.
Findingown data§ Deciphering developmental disease susceptibility using MOSTA · p. 12Wnt5a localized in the craniofacial region, limb, genital ridge, and brain (floor plate and neocortex) from multiple developmental stages, consistent with the disease phenotype.
“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).”
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Findingown data§ Spatial heterogeneity of progenitor cells in the developing dorsal midbrain · p. 10Developmental trajectories revealed two branches arising from radial glia cells, one to neuroblasts and the other to glioblasts.
“The resulting developmental trajectories revealed two branches arising from radial glia cells, one to neuroblasts andthe other to glioblasts (Figure 5D).”
Quote in the paperSubstantial claimProbably not a substantial claim: may not pass S3 (load-bearing), S4 (based). May combine two claims.
Findingnone given§ Limitations of the study · p. 14The 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.
“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.”
Quote in the paperNot a substantial claimMay combine two claims.
Findingown data§ Spatial heterogeneity of progenitor cells in the developing dorsal midbrain · p. 10HES1 was highly active in glioblasts, and its target genes included known glia-lineage genes such as Aldh1l1 and Nfib.
“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 .”
Quote in the paperNot a substantial claimMay combine two claims.
Findingown data§ Spatial heterogeneity of progenitor cells in the developing dorsal midbrain · p. 10Radial glia cells (Mki67+, Dbx1+), the source of both neuroblasts and glioblasts, were the most abundant progenitors at E12.5, with widespread distribution.
“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.”
Quote in the paperNot a substantial claimMay combine two claims.
Findingown data§ Deciphering developmental disease susceptibility using MOSTA · p. 12The MSX1 regulon was more enriched in mesenchymal cells and fibroblasts in the maxilla, and mesenchymal cells in the limb.
“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).”
Quote in the paperSubstantial claimMay combine two claims.
Findingown data§ Deciphering developmental disease susceptibility using MOSTA · p. 12Msx1 was expressed at different levels in multiple cell types of the maxilla including mesenchymal cells, but in the limb it was rather restricted to mesenchymal cells only.
“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.”
Quote in the paperNot a substantial claimClassified as probably not a substantial claim. May combine two claims.
Findingnone given§ DISCUSSION · p. 14The current DNB capture chip costs 220 RMB/mm2 ($35/mm2) or 22,000 RMB/cm2 ($3,500/cm2).
“The current DNB capture chip costs 220 RMB/mm 2 ($35/ mm 2 ) or 22,000 RMB/cm 2 ($3,500/cm 2 ).”
Quote in the paperSubstantial claimMay combine two claims.
Findingown data§ Deciphering developmental disease susceptibility using MOSTA · p. 12Many disease-related genes were mainly enriched in one tissue or system, while others were more widely distributed.
“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.”
Quote in the paperSubstantial claimMay combine two claims.
Findingown data§ Spatial heterogeneity of cell types in mouse embryonic tissues · p. 10The E16.5 Stereo-seq map identified expression of interneuron progenitor markers (Dlx1+, Dlx2+, Lhx6+, Nkx2-1+) within the medial ganglionic eminence and the migratory trajectory of interneurons into the cerebral cortex.
“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).”
Quote in the paperSubstantial claim0 of 1 sources checked- cites 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.
May combine two claims.
Findingown data§ Spatial heterogeneity of cell types in mouse embryonic tissues · p. 10Over 10% of Stereo-seq captured transcripts contained intronic reads, comparable with the proportion captured by scRNA-seq.
“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).”
Quote in the paperSubstantial claim0 of 2 sources checked- cites La Manno et al., 2018 source
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- cites Qiu et al., 2022b
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Probably not a substantial claim: may not pass S3 (load-bearing), S4 (based). May combine two claims.
Findingnone given§ Limitations of the study · p. 14Stereo-seq has genome-wide coverage, but due to limitations in capture, relevant genes with low expression may be missed.
“Similarly, Stereo-seq has a genome-wide coverage, but due to limitations in capture, relevant genes with low expression may be missed.”
Quote in the paperNot a substantial claimMay combine two claims.
Findingown data§ Deciphering developmental disease susceptibility using MOSTA · p. 12Some disease-related genes were expressed in most or all embryonic stages, whereas others had a restricted window.
“Some genes were expressed in most or all embryonic stages, whereas others had a restricted window.”
Quote in the paperNot a substantial claimMay combine two claims.
Findingown data§ Spatial heterogeneity of progenitor cells in the developing dorsal midbrain · p. 10At E12.5, there was only occasional appearance of sparsely distributed neuroblasts, whereas glioblasts localized mostly in the central and caudal parts.
“At this stage, there was only occasional appearance of sparsely distributed neuroblasts, whereas glioblasts localized mostly in the central and caudal parts.”
Quote in the paperSubstantial claimMay combine two claims.
Findingown data§ DISCUSSION · p. 14Although the function of many transcription factors controlling these regulons has been previously reported, the study identified the spatial domains in which they act.
“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.”
Quote in the paperSubstantial claimMay combine two claims.
Findingcitation§ DISCUSSION · p. 14Stereo-seq also captures intronic transcripts, making it possible to calculate spatial RNA velocity with single-cell resolution, which can facilitate the study of cell fate transitions.
“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.”
Quote in the paperSubstantial claim0 of 2 sources checked- cites La Manno et al., 2018 source
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- cites Qiu et al., 2022b
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Findingown data§ Deciphering developmental disease susceptibility using MOSTA · p. 12NKX2-1 was mainly enriched in brain (hippocampus and subpallium) and lung, and Lmod3 was enriched in skeletal muscle.
“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).”
Quote in the paperNot a substantial claim0 of 2 sources checked- cites Shetty et al., 2014
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- cites Berkenstadt et al., 2018
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Probably not a substantial claim: may not pass S3 (load-bearing), S4 (based). May combine two claims.
Findingnone given§ DISCUSSION · p. 14The high-density RNA capture with DNB chip requires a sequencing depth of around 150 gigabases/cm2, which costs 5,000 RMB ($800) based on current short-read sequencing platforms.
“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.”
Quote in the paperNot a substantial claimMay combine two claims.
Findingown data§ Spatial heterogeneity of progenitor cells in the developing dorsal midbrain · p. 10At 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.
“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.”
Quote in the paperSubstantial claimProbably not a substantial claim: may not pass S3 (load-bearing).
Findingown data§ Figure S1. Performance of stereo-seq, related to Figures 1 and 2 · p. 32Regulons with significant spatial autocorrelation were grouped into different modules based on pairwise spatial correlations of an E9.5 section.
“(F) Heatmap showing the regulons with significant spatial autocorrelation grouped into different modules based on pairwise spatial correlations of an E9.5 section (E1S1).”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S3 (load-bearing).
Findingnone given§ Figure S1. Performance of stereo-seq, related to Figures 1 and 2 · p. 26Stereo-seq chips of different sizes range from 50 mm2 to 174.24 cm2.
“(A) Stereo-seq chips of different sizes ranging from 50 mm 2 to 174.24 cm 2 .”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S3 (load-bearing).
Findingown data§ Figure S1. Performance of stereo-seq, related to Figures 1 and 2 · p. 32Genes with significant spatial autocorrelation were grouped into different gene modules based on pairwise spatial correlations of E9.5 and E16.5 sections.
“(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.”
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Findingnone given§ Limitations of the study · p. 14The study profiled a total of 53 sagittal sections corresponding to an overall area of 23.4 cm2, but more sections may still be necessary to provide adequate coverage of all regional tissue types.
“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.”
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Interpretationreasoning§ A · p. 5Stereo-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.
“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.”
Quote in the paperSubstantial claimClassified as probably not a substantial claim.
Premisecitation§ INTRODUCTION · p. 3Recent cell atlases using high-throughput scRNA-seq and scATAC-seq have provided an increasingly detailed view of mouse developmental gene expression and gene regulatory dynamics.
“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”
Quote in the paperSubstantial claim0 of 3 sources checked- cites Cao et al., 2019 source
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- cites He et al., 2020
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- cites Pijuan-Sala et al., 2020
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Interpretationreasoning§ A · p. 5Stereo-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.
“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.”
Quote in the paperSubstantial claimProbably not a substantial claim: may not pass S3 (load-bearing).
Interpretationcitation§ Spatially resolved transcriptomic atlas of mouse organogenesis · p. 7The colocalization of Pantr1 with Sox2 is consistent with the regulatory function of this long noncoding RNA in early neuronal differentiation.
“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).”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites Goff et al., 2015
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Premisenone given§ Figure 3. Spatiotemporal transcriptomic atlas of mouse organogenesis · p. 9Data can be extracted from scRNA-seq datasets using SCENIC, but the lack of spatial information is likely to confound important associations.
“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.”
Quote in the paperSubstantial claim0 of 1 sources checked- cites Aibar et al., 2017 source
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- Rests on an assumption: The lack of spatial information in scRNA-seq data is likely to confound important associations in gene regulatory network analysis.
Probably not a substantial claim: may not pass S3 (load-bearing).
Premisecitation§ Spatially resolved transcriptomic atlas of mouse organogenesis · p. 7Kitl is a pleiotropic factor that acts in the ontogeny of different cell types.
“Kitl is a pleiotropic factor that acts in the ontogeny of different cell types (Broudy, 1997).”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites Broudy, 1997
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Classified as probably not a substantial claim. May combine two claims.
Premisecitation§ INTRODUCTION · p. 3Early mouse embryos, specific late-stage-embryonic tissues such as the cerebral cortex, and whole midgestation embryos have been analyzed using spatially resolved transcriptomic approaches.
“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.”
Quote in the paperSubstantial claim0 of 3 sources checked- cites Lohoff et al., 2022
Not checked in the free run.
- cites Di Bella et al., 2021
Not checked in the free run.
- cites Liu et al., 2020 source
Not checked in the free run.
Probably not a substantial claim: may not pass S4 (based).
Interpretationreasoning§ Highlights · p. 1Stereo-seq defines the spatiotemporal window of developmental disease vulnerability.
“Stereo-seq defines the spatiotemporal window of developmental disease vulnerability”
Quote in the paperNot a substantial claimMay combine two claims.
Interpretationown data§ Resource Cell · p. 14The results demonstrate the utility of MOSTA for defining spatiotemporal windows of disease-related gene expression during development and for establishing potential vulnerabilities.
“These results demonstrate the utility of MOSTA for defining the spatiotemporal windows of disease-related gene expression during development and for establishing potential vulnerabilities.”
Quote in the paperSubstantial claimProbably not a substantial claim: may not pass S4 (based). May combine two claims.
Premisenone given§ DISCUSSION · p. 14Stereo-seq is a DNB-based spatially resolved transcriptomic technology with genome-wide coverage, cellular resolution, high sensitivity, and large field of view.
“Stereo-seq is a DNB-based spatially resolved transcriptomic technology with genome-wide coverage, cellular resolution, high sensitivity, and large field of view.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S4 (based).
Interpretationreasoning§ DISCUSSION · p. 14These parameters are all critical for accurately profiling the transcriptomic heterogeneity of relatively large mid- and late-stage mammalian embryos.
“These parameters are all critical for accurately profiling the transcriptomic heterogeneity of relatively large mid- and late-stage mammalian embryos.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S2 (checkable), S3 (load-bearing), S4 (based).
Interpretationnone given§ DISCUSSION · p. 14Due to its characteristics and applicability, Stereo-seq also has the potential to move into routine clinical practice as a diagnostic tool complementary to medical imaging and histopathology data.
“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.”
Quote in the paperNot a substantial claimMay combine two claims.
Interpretationown data§ Spatial heterogeneity of cell types in mouse embryonic tissues · p. 10MOSTA comprehensively characterizes cell-type and cell-state heterogeneity at the whole embryo level, which is relevant for reconstructing cell differentiation processes involving regional and subregional specificities.
“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.”
Quote in the paperSubstantial claim- 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.
Probably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.
Premisecitation§ Deciphering developmental disease susceptibility using MOSTA · p. 12Msx1 is a homeobox gene encoding a transcription factor with known crosstalk with WNT5A.
“We then investigated Msx1 , a homeobox gene encoding a transcription factor with known crosstalk with WNT5A (Lin et al., 2011).”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites Lin et al., 2011
Not checked in the free run.
Classified as probably not a substantial claim.
Premisecitation§ Deciphering developmental disease susceptibility using MOSTA · p. 12scRNA-seq datasets are becoming widely used to predict the origin and mechanisms of genetic diseases including developmental disorders.
“Nowadays, scRNA-seq datasets are becoming widely used to predict the origin and mechanisms of genetic diseases including developmental disorders (Watanabe et al., 2019).”
Quote in the paperSubstantial claim0 of 1 sources checked- cites Watanabe et al., 2019
Not checked in the free run.
Probably not a substantial claim: may not pass S2 (checkable), S3 (load-bearing), S4 (based).
Interpretationnone given§ DISCUSSION · p. 14Besides the study of development, Stereo-seq and its future refinements have the ability to transform multiple other research fields.
“Besides the study of development, Stereo-seq and its future refinements have the ability to transform multiple other research fields.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S3 (load-bearing).
Premisecitation§ Spatial heterogeneity of progenitor cells in the developing dorsal midbrain · p. 10The dorsal midbrain contains complex multilayered structures like the cerebral cortex that form the superior and inferior colliculi.
“The dorsal midbrain contains complex multilayered structures like the cerebral cortex that form the superior and inferior colliculi (Arimura et al., 2019).”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites Arimura et al., 2019
Not checked in the free run.
Probably not a substantial claim: may not pass S2 (checkable), S3 (load-bearing). May combine two claims.
Interpretationnone given§ DISCUSSION · p. 14Mice and humans differ in multiple aspects of normal physiology and disease; hence, there are caveats, but the approach clearly has the potential to uncover disease mechanisms.
“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.”
Quote in the paperNot a substantial claim- Rests on an assumption: Mice and humans differ in multiple aspects of normal physiology and disease.
May combine two claims.
Interpretationown data§ Spatial heterogeneity of progenitor cells in the developing dorsal midbrain · p. 10The 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.
“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.”
Quote in the paperSubstantial claim- 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.
Probably not a substantial claim: may not pass S3 (load-bearing).
Study design and conductreasoning§ Animal · p. 21All relevant procedures involving animal experiments presented in this study are compliant with ethical regulations regarding animal research.
“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).”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S3 (load-bearing).
Study design and conductnone given§ Stereo-seq raw data processing · p. 22Reads with MID containing either N bases or more than 2 bases with quality score lower than 10 were filtered out.
“Reads with MID containing either N bases or more than 2 bases with quality score lower than 10 were filtered out.”
Quote in the paperNot a substantial claimClassified as probably not a substantial claim.
Study design and conductnone given§ Stereo-seq raw data processing · p. 22UMI with the same CID and the same gene locus were collapsed, allowing 1 mismatch to correct for sequencing and PCR errors.
“UMI with the same CID and the same gene locus were collapsed, allowing 1 mismatch to correct for sequencing and PCR errors.”
Quote in the paperSubstantial claimProbably not a substantial claim: may not pass S3 (load-bearing).
Study design and conductnone given§ Stereo-seq raw data processing · p. 22The whole Stereo-seq data processing procedure was integrated into a publicly available pipeline SAW.
“The whole procedure was integrated into a publicly available pipeline SAW available at https://github.com/BGIResearch/SAW.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S3 (load-bearing).
Study design and conductnone given§ Animal · p. 21All relevant procedures involving animal experiments presented in this study 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).
“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).”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S2 (checkable), S3 (load-bearing), S4 (based).
Interpretationnone given§ Limitations of the study · p. 14Although algorithms including SCC were developed for the analyses in this study, others may be needed for a broader range of applications in various tissues from different species.
“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.”
Quote in the paperNot a substantial claimClassified as probably not a substantial claim.
Study design and conductreasoning§ Comparison of Stereo-seq with published methods · p. 23For Figure 1C, to ensure proper comparisons, Stereo-seq data were binned into bin 3 (3x3 DNB, 2 mm), bin 14 (14x14 DNB, 10 mm) or bin 140 (140x140 DNB, 100 mm).
“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).”
Quote in the paperSubstantial claimClassified as probably not a substantial claim.
Study design and conductnone given§ Stereo-seq raw data processing · p. 22CID sequences on the first reads were mapped to the designed coordinates of the in situ captured chip, allowing 1 base mismatch to correct for sequencing and PCR errors.
“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.”
Quote in the paperSubstantial claimClassified as probably not a substantial claim.
Study design and conductcitation§ Cell type mapping · p. 24To validate cell type identity in the adult mouse hemibrain section, annotated scRNA-seq profiles of the adult mouse brain were used for comparison.
“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.”
Quote in the paperSubstantial claim0 of 1 sources checked- cites Zeisel et al., 2018 source
Not checked in the free run.
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?
Conventional microscopy is compatible with these transcriptomic procedures, as demonstrated by Seq-Scope with H&E histology and Stereo-Seq with DAPI fluorescence.
“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]
The Tissue processing section (p. 21) reports staining Stereo-seq sections with a nucleic acid dye and imaging them by microscope before RNA capture, supporting compatibility with conventional imaging. It does not identify the dye as DAPI. The paper’s Seq-Scope comparison (Figure 1, p. 4) does not address H&E histology; that example has a separate citation in the citing sentence. (LLM, conf=0.87)
Experimental — accuracy not yet measuredPreviously, spatial transcriptomics technology has been utilized to create spatiotemporal maps of various life stages, including mouse brain, regenerating axolotl telencephalons, aging ovary, and brain.
“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]
The Summary (p. 2) says Stereo-seq was used to create a spatiotemporal atlas of mouse organogenesis, and the atlas section (p. 7) describes maps of developing mouse brains. Our copy does not support the cited sentence’s examples of regenerating axolotl telencephalons or aging ovaries; its mention of an ovary concerns embryonic mouse tissue, not aging. (LLM, conf=0.88)
Experimental — accuracy not yet measuredStereo-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.
“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]
High-resolution methods Seq-Scope, Stereo-seq and PIXEL-seq provide submicrometer resolution and highly efficient transcriptome-capture efficiency up to ~23 unique transcripts per μm2.
“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]
Spatially resolved transcriptomics links cellular position to sequencing data and offers an in-depth view of gene expression within tissues.
“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]
Current spatial technologies capture only one layer of omics information at a time, precluding examination of mechanistic relationships across the central dogma.
“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]
Spatial epigenomics, transcriptomics and proteomics emerged recently but most capture only one layer of omics information.
“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]
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.”
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Self-citationSpatially resolved mapping of cells associated with human complex traits
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Self-citationSpatial mapping of cellular senescence: emerging challenges and opportunities
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Self-citationSpatial oncology: Translating contextual biology to the clinic
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Single-cell and spatial atlases of spinal cord injury in the Tabulae Paralytica
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THItoGene: a deep learning method for predicting spatial transcriptomics from histological images
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SPACEL: deep learning-based characterization of spatial transcriptome architectures
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Self-citationSpatial omics technologies at multimodal and single cell/subcellular level
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Computational solutions for spatial transcriptomics
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Spatially exploring RNA biology in archival formalin-fixed paraffin-embedded tissues
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Self-citationSpatiotemporal modeling of molecular holograms
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Self-citationYu, L., Logsdon, D. M., Pinzón-Arteaga, C. A., Duan, J., Ezashi, T., Wei, Y., Orsi, A. E. R., Oura, S., Liu, L., Wang, L., et al. (2023) · Cell stem cell · doi:10.1016/j.stem.2023.08.002 · Cited by 117 · see citing works
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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- 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 · 2020: 1
- 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
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