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single cell transcriptomics datasets for lgg cancer type  (Broad Institute Inc)

 
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    Broad Institute Inc single cell transcriptomics datasets for lgg cancer type
    PANoptosis has a prognostic impact in cancers. ( A ) Consensus Clustering showing three distinct clusters (PANoptosis low, PANoptosis medium and PANoptosis high) based on PANoptosis gene expression <t>for</t> <t>SKCM.</t> ( B ) Heatmap depicting gene expression profiles of 27 PANoptosis markers including sensors and upstream regulators, adaptors and effectors of PANoptosis as scaled Z-scores for SKCM tumor samples. For brevity, 13 out of the 27 genes are labeled, but 27 distinct rows are shown. ( C ) Boxplot showing the distribution of PANoptosis scores in the three PANoptosis clusters for cancer subtypes of interest: <t>LGG,</t> KIRC and SKCM. ( D ) Forest plot showing N1 = number of samples in PANoptosis high cluster, N2 = number of samples in PANoptosis low cluster, P -value and hazard ratio (HR) with 95% CI for overall survival (OS) when comparing PANoptosis high versus low for each cancer type where there is significant prognostic impact ( P -value < 0.05). ( E–G ) Kaplan–Meier curves showing OS across the PANoptosis high and PANoptosis low groups in the three cancer types with significant differences in survival (PANoptosis high beneficial [HR < 1] or detrimental [HR > 1]). *** P -value < 0.001.
    Single Cell Transcriptomics Datasets For Lgg Cancer Type, supplied by Broad Institute Inc, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
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    1) Product Images from "Pancancer transcriptomic profiling identifies key PANoptosis markers as therapeutic targets for oncology"

    Article Title: Pancancer transcriptomic profiling identifies key PANoptosis markers as therapeutic targets for oncology

    Journal: NAR Cancer

    doi: 10.1093/narcan/zcac033

    PANoptosis has a prognostic impact in cancers. ( A ) Consensus Clustering showing three distinct clusters (PANoptosis low, PANoptosis medium and PANoptosis high) based on PANoptosis gene expression for SKCM. ( B ) Heatmap depicting gene expression profiles of 27 PANoptosis markers including sensors and upstream regulators, adaptors and effectors of PANoptosis as scaled Z-scores for SKCM tumor samples. For brevity, 13 out of the 27 genes are labeled, but 27 distinct rows are shown. ( C ) Boxplot showing the distribution of PANoptosis scores in the three PANoptosis clusters for cancer subtypes of interest: LGG, KIRC and SKCM. ( D ) Forest plot showing N1 = number of samples in PANoptosis high cluster, N2 = number of samples in PANoptosis low cluster, P -value and hazard ratio (HR) with 95% CI for overall survival (OS) when comparing PANoptosis high versus low for each cancer type where there is significant prognostic impact ( P -value < 0.05). ( E–G ) Kaplan–Meier curves showing OS across the PANoptosis high and PANoptosis low groups in the three cancer types with significant differences in survival (PANoptosis high beneficial [HR < 1] or detrimental [HR > 1]). *** P -value < 0.001.
    Figure Legend Snippet: PANoptosis has a prognostic impact in cancers. ( A ) Consensus Clustering showing three distinct clusters (PANoptosis low, PANoptosis medium and PANoptosis high) based on PANoptosis gene expression for SKCM. ( B ) Heatmap depicting gene expression profiles of 27 PANoptosis markers including sensors and upstream regulators, adaptors and effectors of PANoptosis as scaled Z-scores for SKCM tumor samples. For brevity, 13 out of the 27 genes are labeled, but 27 distinct rows are shown. ( C ) Boxplot showing the distribution of PANoptosis scores in the three PANoptosis clusters for cancer subtypes of interest: LGG, KIRC and SKCM. ( D ) Forest plot showing N1 = number of samples in PANoptosis high cluster, N2 = number of samples in PANoptosis low cluster, P -value and hazard ratio (HR) with 95% CI for overall survival (OS) when comparing PANoptosis high versus low for each cancer type where there is significant prognostic impact ( P -value < 0.05). ( E–G ) Kaplan–Meier curves showing OS across the PANoptosis high and PANoptosis low groups in the three cancer types with significant differences in survival (PANoptosis high beneficial [HR < 1] or detrimental [HR > 1]). *** P -value < 0.001.

    Techniques Used: Gene Expression, Labeling

    TCGA cancer abbreviations. Cancers of interest are highlighted in colors
    Figure Legend Snippet: TCGA cancer abbreviations. Cancers of interest are highlighted in colors

    Techniques Used:

    Multiple survival models identify key prognostic PANoptosis markers for LGG, KIRC and SKCM. ( A ) Forest plot for key PANoptosis genes whose high expression leads to a poor prognosis for LGG identified through univariate survival models. ( B ) PANoptosis genes with non-zero coefficients and the fraction of times they appeared during the 100 random runs of the GLMnet model for LGG. ( C ) Top 10 PANoptosis genes with highest prognostic relevance determined by the optimal RFS model for LGG. ( D ) Forest plot for key PANoptosis genes whose high expression leads to a poor prognosis for KIRC identified through univariate survival models. ( E ) PANoptosis genes with non-zero coefficients and the fraction of times they appeared during the 100 random runs of the GLMnet model for KIRC. ( F ) Top 10 PANoptosis genes with highest prognostic relevance determined by the optimal RFS model for KIRC. ( G ) Forest plot for key PANoptosis genes whose high expression leads to better prognosis for SKCM identified by univariate survival models. ( H ) PANoptosis genes with non-zero coefficients and the fraction of times they appeared during the 100 random runs of the GLMnet model for SKCM. ( I ) Top 10 PANoptosis genes with highest prognostic relevance determined by the optimal RFS model for SKCM. (A–I) Blue bars represent a negative coefficient (higher expression is beneficial for survival), and red bars represent a positive coefficient (higher expression is detrimental for survival). The orange boxes highlight the genes which are prognostic across the univariate, GLMNet and RFS survival models and were considered as the ‘Top’ PANoptosis markers. (B, C, E, F, H, I) The boxplots correspond to variable importance estimated using a subsampling approach.
    Figure Legend Snippet: Multiple survival models identify key prognostic PANoptosis markers for LGG, KIRC and SKCM. ( A ) Forest plot for key PANoptosis genes whose high expression leads to a poor prognosis for LGG identified through univariate survival models. ( B ) PANoptosis genes with non-zero coefficients and the fraction of times they appeared during the 100 random runs of the GLMnet model for LGG. ( C ) Top 10 PANoptosis genes with highest prognostic relevance determined by the optimal RFS model for LGG. ( D ) Forest plot for key PANoptosis genes whose high expression leads to a poor prognosis for KIRC identified through univariate survival models. ( E ) PANoptosis genes with non-zero coefficients and the fraction of times they appeared during the 100 random runs of the GLMnet model for KIRC. ( F ) Top 10 PANoptosis genes with highest prognostic relevance determined by the optimal RFS model for KIRC. ( G ) Forest plot for key PANoptosis genes whose high expression leads to better prognosis for SKCM identified by univariate survival models. ( H ) PANoptosis genes with non-zero coefficients and the fraction of times they appeared during the 100 random runs of the GLMnet model for SKCM. ( I ) Top 10 PANoptosis genes with highest prognostic relevance determined by the optimal RFS model for SKCM. (A–I) Blue bars represent a negative coefficient (higher expression is beneficial for survival), and red bars represent a positive coefficient (higher expression is detrimental for survival). The orange boxes highlight the genes which are prognostic across the univariate, GLMNet and RFS survival models and were considered as the ‘Top’ PANoptosis markers. (B, C, E, F, H, I) The boxplots correspond to variable importance estimated using a subsampling approach.

    Techniques Used: Expressing

    Survival models built using key PANoptosis markers predict survival on independent test sets. ( A ) Comparison of AUC metric at t ∈ {2,4,5} years between Coxnet, GLMnet and RFS survival models for LGG. ( B ) Comparison of AUC metric at t ∈ {2,3,5} years between Coxnet, GLMnet and RFS survival models for KIRC. ( C ) Comparison of AUC metric at t ∈ {1,2,3} years between Coxnet, GLMnet and RFS models for SKCM.
    Figure Legend Snippet: Survival models built using key PANoptosis markers predict survival on independent test sets. ( A ) Comparison of AUC metric at t ∈ {2,4,5} years between Coxnet, GLMnet and RFS survival models for LGG. ( B ) Comparison of AUC metric at t ∈ {2,3,5} years between Coxnet, GLMnet and RFS survival models for KIRC. ( C ) Comparison of AUC metric at t ∈ {1,2,3} years between Coxnet, GLMnet and RFS models for SKCM.

    Techniques Used: Comparison

    Single cell transcriptomics provides evidence for PANoptosis in individual cells in LGG and SKCM datasets. ( A ) Expression profiles of PANoptosis genes across different cell types in the LGG dataset. ( B ) PANoptosis activity across different cell types in the LGG dataset estimated using ssGSEA. ( C ) Expression profiles of PANoptosis genes across different cell types for the SKCM dataset. ( D ) PANoptosis activity across different cell types in the SKCM dataset estimated using ssGSEA.
    Figure Legend Snippet: Single cell transcriptomics provides evidence for PANoptosis in individual cells in LGG and SKCM datasets. ( A ) Expression profiles of PANoptosis genes across different cell types in the LGG dataset. ( B ) PANoptosis activity across different cell types in the LGG dataset estimated using ssGSEA. ( C ) Expression profiles of PANoptosis genes across different cell types for the SKCM dataset. ( D ) PANoptosis activity across different cell types in the SKCM dataset estimated using ssGSEA.

    Techniques Used: Single-cell Transcriptomics, Expressing, Activity Assay

    Related Articles

    Single-cell Transcriptomics:

    Article Title: Pancancer transcriptomic profiling identifies key PANoptosis markers as therapeutic targets for oncology
    Article Snippet: .. Single cell transcriptomics datasets for LGG and SKCM cancer types were downloaded from the Broad Institute Single Cell Portal under accession number SCP271 ( ) and GEO Accession viewer under accession ID GSE72056 , respectively. ..

    Single Cell:

    Article Title: Pancancer transcriptomic profiling identifies key PANoptosis markers as therapeutic targets for oncology
    Article Snippet: .. Single cell transcriptomics datasets for LGG and SKCM cancer types were downloaded from the Broad Institute Single Cell Portal under accession number SCP271 ( ) and GEO Accession viewer under accession ID GSE72056 , respectively. ..



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    single cell transcriptomes methods  (10X Genomics)
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    10X Genomics single cell transcriptomes methods
    a , Optic lobe cross-section , with drawings of unicolumnar (orange shades) and multicolumnar (blue) neurons. Dashed lines: boundaries between layers. A: anterior, L: lateral, M: medial, P: posterior. b, Approach followed to produce the adult dataset. c, Pearson correlation between the average gene expression of the adult dataset clusters (x-axis) and the <t>transcriptome</t> of isolated Lawf1 neurons (Methods). d, tSNE visualization of the final adult dataset, using 120 principal components calculated on the log-normalized integrated gene expression. The 61 identified neuronal clusters are labeled by their standard abbreviation, G1–16: glial clusters, LQ: low-quality cells, G/LQ1–4: glial clusters with some features of low-quality cells, *: clusters with less confident annotations . e, Approximate time frames of different steps of optic lobe development, and tSNE visualizations of the pupal datasets. Colors match to the adult dataset as classified by the neural network. f, Multi-task neural network classifier used at each stage to sequentially match developing cells to the adult clusters, as detailed in .
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    a , Optic lobe cross-section , with drawings of unicolumnar (orange shades) and multicolumnar (blue) neurons. Dashed lines: boundaries between layers. A: anterior, L: lateral, M: medial, P: posterior. b, Approach followed to produce the adult dataset. c, Pearson correlation between the average gene expression of the adult dataset clusters (x-axis) and the transcriptome of isolated Lawf1 neurons (Methods). d, tSNE visualization of the final adult dataset, using 120 principal components calculated on the log-normalized integrated gene expression. The 61 identified neuronal clusters are labeled by their standard abbreviation, G1–16: glial clusters, LQ: low-quality cells, G/LQ1–4: glial clusters with some features of low-quality cells, *: clusters with less confident annotations . e, Approximate time frames of different steps of optic lobe development, and tSNE visualizations of the pupal datasets. Colors match to the adult dataset as classified by the neural network. f, Multi-task neural network classifier used at each stage to sequentially match developing cells to the adult clusters, as detailed in .

    Journal: Nature

    Article Title: Neuronal diversity and convergence in a visual system developmental atlas

    doi: 10.1038/s41586-020-2879-3

    Figure Lengend Snippet: a , Optic lobe cross-section , with drawings of unicolumnar (orange shades) and multicolumnar (blue) neurons. Dashed lines: boundaries between layers. A: anterior, L: lateral, M: medial, P: posterior. b, Approach followed to produce the adult dataset. c, Pearson correlation between the average gene expression of the adult dataset clusters (x-axis) and the transcriptome of isolated Lawf1 neurons (Methods). d, tSNE visualization of the final adult dataset, using 120 principal components calculated on the log-normalized integrated gene expression. The 61 identified neuronal clusters are labeled by their standard abbreviation, G1–16: glial clusters, LQ: low-quality cells, G/LQ1–4: glial clusters with some features of low-quality cells, *: clusters with less confident annotations . e, Approximate time frames of different steps of optic lobe development, and tSNE visualizations of the pupal datasets. Colors match to the adult dataset as classified by the neural network. f, Multi-task neural network classifier used at each stage to sequentially match developing cells to the adult clusters, as detailed in .

    Article Snippet: To produce an exhaustive catalog of neurons in the adult optic lobe ( – ), we obtained 109,743 single-cell transcriptomes (Methods) using the Chromium system (10x Genomics).

    Techniques: Gene Expression, Isolation, Labeling

    a. The proportions of UMIs from mitochondrial genes per cell (n = number of cells in each library, indicated on the right) and the total number of cells passing filters in each of the 15 libraries comprising the adult dataset. Names indicated correspond to the names in the Seurat object provided (Adult.rds, GSE142787). Boxplots display the first, second and third quartiles. Whiskers extend from the box to the highest or lowest values in the 1.5 inter-quartile range, and outlying datapoints are represented by a dot. b, Origin of the cells in the final adult clusters, colored as in (a). Green arrows: clusters whose unique library distribution can be explained by variable contamination from surrounding tissues (cluster 3 is photoreceptors, 112 is likely Kenyon Cells from the central brain) or the number of lamina neuropils dissociated (clusters 107, 108, 109 are lamina neurons). Red arrows: clusters likely enriched in low quality transcriptomes, as they are enriched in cells from libraries with high number of mitochondrial genes (38, 120, 192) or high number of cells sequenced (102, likely corresponding to multiplets). Brackets: Glial clusters, some of them enriched in libraries with high number of mitochondrial genes as ambient RNA is more similar to RNA from glial vs . neuronal cells . c, Number of clusters obtained with different pairs of clustering parameters. Red rectangle: pair of parameters used. d, Left: Legend as in . Right: Number of isolated neuronal type transcriptomes matching to 1–5 of our adult clusters, for each pair of parameters in (c), which we used as a measure of the biological relevance of our clusters. Matching was defined by the presence of a correlation gap above 0.05 (Methods). We took into account any correlation gap between the 6 best correlated clusters, since similar cell types or overclustering can affect the size of the first correlation gap as illustrated on the left graphs. Red rectangle: pair of parameters used. e, tSNE visualization of the adult optic lobe single-cell transcriptomes, using 120 principal components calculated on the log-normalized integrated gene expression. Cell colors indicate the cluster they belonged to before we merged artificially split clusters (red circles, Methods). f, Heatmap showing scaled log-normalized non-integrated expression of the top20 cluster markers between the merged clusters. Merged clusters had almost indistinguishable gene expression patterns, but often differed by their proportions of UMI from mitochondrial genes per cell or the expression levels of the genes highlighted in red, which are enriched in the “ambient RNA cluster” 192 (see also ).

    Journal: Nature

    Article Title: Neuronal diversity and convergence in a visual system developmental atlas

    doi: 10.1038/s41586-020-2879-3

    Figure Lengend Snippet: a. The proportions of UMIs from mitochondrial genes per cell (n = number of cells in each library, indicated on the right) and the total number of cells passing filters in each of the 15 libraries comprising the adult dataset. Names indicated correspond to the names in the Seurat object provided (Adult.rds, GSE142787). Boxplots display the first, second and third quartiles. Whiskers extend from the box to the highest or lowest values in the 1.5 inter-quartile range, and outlying datapoints are represented by a dot. b, Origin of the cells in the final adult clusters, colored as in (a). Green arrows: clusters whose unique library distribution can be explained by variable contamination from surrounding tissues (cluster 3 is photoreceptors, 112 is likely Kenyon Cells from the central brain) or the number of lamina neuropils dissociated (clusters 107, 108, 109 are lamina neurons). Red arrows: clusters likely enriched in low quality transcriptomes, as they are enriched in cells from libraries with high number of mitochondrial genes (38, 120, 192) or high number of cells sequenced (102, likely corresponding to multiplets). Brackets: Glial clusters, some of them enriched in libraries with high number of mitochondrial genes as ambient RNA is more similar to RNA from glial vs . neuronal cells . c, Number of clusters obtained with different pairs of clustering parameters. Red rectangle: pair of parameters used. d, Left: Legend as in . Right: Number of isolated neuronal type transcriptomes matching to 1–5 of our adult clusters, for each pair of parameters in (c), which we used as a measure of the biological relevance of our clusters. Matching was defined by the presence of a correlation gap above 0.05 (Methods). We took into account any correlation gap between the 6 best correlated clusters, since similar cell types or overclustering can affect the size of the first correlation gap as illustrated on the left graphs. Red rectangle: pair of parameters used. e, tSNE visualization of the adult optic lobe single-cell transcriptomes, using 120 principal components calculated on the log-normalized integrated gene expression. Cell colors indicate the cluster they belonged to before we merged artificially split clusters (red circles, Methods). f, Heatmap showing scaled log-normalized non-integrated expression of the top20 cluster markers between the merged clusters. Merged clusters had almost indistinguishable gene expression patterns, but often differed by their proportions of UMI from mitochondrial genes per cell or the expression levels of the genes highlighted in red, which are enriched in the “ambient RNA cluster” 192 (see also ).

    Article Snippet: To produce an exhaustive catalog of neurons in the adult optic lobe ( – ), we obtained 109,743 single-cell transcriptomes (Methods) using the Chromium system (10x Genomics).

    Techniques: Isolation, Gene Expression, Expressing

    a, Pearson correlation between the average log-normalized non-integrated expression of the top10 cluster markers of the adult dataset clusters (x-axis) and the transcriptome of isolated Repo+ (glial marker) or Elav+ (neuronal marker) populations. LQ = clusters containing a proportion of cells with features of lower quality transcriptomes. b, Violin plots of features tending to be higher (proportions of UMI from mitochondrial genes) or lower (number of UMIs or genes per cell) in low quality cells , . c, Heatmap showing the scaled log-normalized non-integrated expression of the top5 cluster markers of the adult dataset. The first 5 neuronal adult clusters (1 to 6, cluster 1 and 2 having been merged) are plotted for reference as they clearly have specific gene expression patterns. Clusters 38, 85, 102 and 120 present much less defined gene expression patterns and likely contain low quality neuronal transcriptomes (see also ). Clusters 188 and 189 could be further separated in two groups with different gene expression patterns, as illustrated by the dashed line in the insert. Cluster 191 expresses several markers found in no other clusters and likely correspond to neither glia nor optic-lobe neuron. Cluster 192 expresses mainly low levels of glia-specific genes, without specific markers. It likely corresponds to ambient RNA, which would be enriched in RNA from burst glial cells.

    Journal: Nature

    Article Title: Neuronal diversity and convergence in a visual system developmental atlas

    doi: 10.1038/s41586-020-2879-3

    Figure Lengend Snippet: a, Pearson correlation between the average log-normalized non-integrated expression of the top10 cluster markers of the adult dataset clusters (x-axis) and the transcriptome of isolated Repo+ (glial marker) or Elav+ (neuronal marker) populations. LQ = clusters containing a proportion of cells with features of lower quality transcriptomes. b, Violin plots of features tending to be higher (proportions of UMI from mitochondrial genes) or lower (number of UMIs or genes per cell) in low quality cells , . c, Heatmap showing the scaled log-normalized non-integrated expression of the top5 cluster markers of the adult dataset. The first 5 neuronal adult clusters (1 to 6, cluster 1 and 2 having been merged) are plotted for reference as they clearly have specific gene expression patterns. Clusters 38, 85, 102 and 120 present much less defined gene expression patterns and likely contain low quality neuronal transcriptomes (see also ). Clusters 188 and 189 could be further separated in two groups with different gene expression patterns, as illustrated by the dashed line in the insert. Cluster 191 expresses several markers found in no other clusters and likely correspond to neither glia nor optic-lobe neuron. Cluster 192 expresses mainly low levels of glia-specific genes, without specific markers. It likely corresponds to ambient RNA, which would be enriched in RNA from burst glial cells.

    Article Snippet: To produce an exhaustive catalog of neurons in the adult optic lobe ( – ), we obtained 109,743 single-cell transcriptomes (Methods) using the Chromium system (10x Genomics).

    Techniques: Expressing, Isolation, Marker, Gene Expression

    a, Pearson correlation between the average log-normalized non-integrated expression of the top10 cluster markers of the adult dataset clusters (x-axis) and the transcriptome of isolated neurons , . We represented Dm3, Tm9, T4 and T5 before their split into Dm3a/b, Tm9v/d, T4/T5ab and T4/T5cd. When two transcriptomes were published for a given neuronal type, the one presenting the highest correlation gap is displayed in this figure. R1–8: average gene expression of all photoreceptors . KC: Kenyon Cells, cluster 112 therefore likely corresponds to contamination from the central brain. b, Legend as in (a). We indicated several matching clusters to highlight the high similarity between LC cells transcriptomes, which explains the lower correlation gaps observed for these neurons. c, Left: Legend as in (a). Right: mixture modelling of Pm3 markers (y axis). Clusters are spread on the x-axis, with the probability of expression of the markers figured by the size of the black dots.

    Journal: Nature

    Article Title: Neuronal diversity and convergence in a visual system developmental atlas

    doi: 10.1038/s41586-020-2879-3

    Figure Lengend Snippet: a, Pearson correlation between the average log-normalized non-integrated expression of the top10 cluster markers of the adult dataset clusters (x-axis) and the transcriptome of isolated neurons , . We represented Dm3, Tm9, T4 and T5 before their split into Dm3a/b, Tm9v/d, T4/T5ab and T4/T5cd. When two transcriptomes were published for a given neuronal type, the one presenting the highest correlation gap is displayed in this figure. R1–8: average gene expression of all photoreceptors . KC: Kenyon Cells, cluster 112 therefore likely corresponds to contamination from the central brain. b, Legend as in (a). We indicated several matching clusters to highlight the high similarity between LC cells transcriptomes, which explains the lower correlation gaps observed for these neurons. c, Left: Legend as in (a). Right: mixture modelling of Pm3 markers (y axis). Clusters are spread on the x-axis, with the probability of expression of the markers figured by the size of the black dots.

    Article Snippet: To produce an exhaustive catalog of neurons in the adult optic lobe ( – ), we obtained 109,743 single-cell transcriptomes (Methods) using the Chromium system (10x Genomics).

    Techniques: Expressing, Isolation, Gene Expression

    a-b, tSNE visualization of the P70 optic lobe single-cell transcriptomes, using 120 principal components calculated on the log-normalized integrated gene expression. Cells colors indicate the clusters they belonged to according to unsupervised clustering (a), or the adult clusters they were classified as by the neural network (b, same as in ). Black circles indicate high granularity regions, where less frequent cell types were grouped together by unsupervised clustering but could be resolved accurately by the neural network (b). c, Same as in (a-b) but cells are named and colored by the adult cluster they were classified as by Seurat label transfer (Methods). d, tSNE visualizations (same as c) including only the cells that were assigned inconsistent identities by Seurat and the neural network. Highest rates of inconsistencies were observed in the center (LQ cells), in L1 and L2 clusters (red ellipses), in most glia clusters (green ellipses), the TE neurons and a glia-like cluster (identity 214, ) with no adult correspondence (blue ellipses). e-f, tSNE visualizations of 56,902 cells sequenced from whole fly brains , using 120 principal components calculated on the log-normalized gene expression. e, Cells are named and colored by the clusters they were classified as by our neural network. f, Cells are named by the cluster identities from the original study and colored by the confidence score they received from our neural network. Black circles mark the following central brain clusters (from left to right): Poxn, OPN, clock neurons and dopaminergic neurons, that all received low scores from the neural network. Kenyon cells (red circles) were assigned with high confidence as our adult dataset was contaminated by them (cluster 112).

    Journal: Nature

    Article Title: Neuronal diversity and convergence in a visual system developmental atlas

    doi: 10.1038/s41586-020-2879-3

    Figure Lengend Snippet: a-b, tSNE visualization of the P70 optic lobe single-cell transcriptomes, using 120 principal components calculated on the log-normalized integrated gene expression. Cells colors indicate the clusters they belonged to according to unsupervised clustering (a), or the adult clusters they were classified as by the neural network (b, same as in ). Black circles indicate high granularity regions, where less frequent cell types were grouped together by unsupervised clustering but could be resolved accurately by the neural network (b). c, Same as in (a-b) but cells are named and colored by the adult cluster they were classified as by Seurat label transfer (Methods). d, tSNE visualizations (same as c) including only the cells that were assigned inconsistent identities by Seurat and the neural network. Highest rates of inconsistencies were observed in the center (LQ cells), in L1 and L2 clusters (red ellipses), in most glia clusters (green ellipses), the TE neurons and a glia-like cluster (identity 214, ) with no adult correspondence (blue ellipses). e-f, tSNE visualizations of 56,902 cells sequenced from whole fly brains , using 120 principal components calculated on the log-normalized gene expression. e, Cells are named and colored by the clusters they were classified as by our neural network. f, Cells are named by the cluster identities from the original study and colored by the confidence score they received from our neural network. Black circles mark the following central brain clusters (from left to right): Poxn, OPN, clock neurons and dopaminergic neurons, that all received low scores from the neural network. Kenyon cells (red circles) were assigned with high confidence as our adult dataset was contaminated by them (cluster 112).

    Article Snippet: To produce an exhaustive catalog of neurons in the adult optic lobe ( – ), we obtained 109,743 single-cell transcriptomes (Methods) using the Chromium system (10x Genomics).

    Techniques: Gene Expression

    tSNE visualizations of all optic lobe single-cell transcriptomes acquired for this study, using 120 principal components calculated on the log-normalized integrated gene expression. The cells are named and colored consistently at all stages by the neural network classifications with manual adjustments as detailed in . Blue ellipses: Dm3 and Tm9 neuronal subtypes, which could only be resolved at P50 and earlier.

    Journal: Nature

    Article Title: Neuronal diversity and convergence in a visual system developmental atlas

    doi: 10.1038/s41586-020-2879-3

    Figure Lengend Snippet: tSNE visualizations of all optic lobe single-cell transcriptomes acquired for this study, using 120 principal components calculated on the log-normalized integrated gene expression. The cells are named and colored consistently at all stages by the neural network classifications with manual adjustments as detailed in . Blue ellipses: Dm3 and Tm9 neuronal subtypes, which could only be resolved at P50 and earlier.

    Article Snippet: To produce an exhaustive catalog of neurons in the adult optic lobe ( – ), we obtained 109,743 single-cell transcriptomes (Methods) using the Chromium system (10x Genomics).

    Techniques: Gene Expression

    a-b, tSNE visualization of the P70 optic lobe single-cell transcriptomes, using 120 principal components calculated on the log-normalized integrated gene expression. Cells are named by the unsupervised cluster they were assigned to and colored by (a) the confidence score they received from the neural network (NN) or by (b) the log-normalized non-integrated expression of Fs (green), dimm (blue), and skl (red), which are co-expressed in TE neurons (red ellipses). c , Violin plot of log-normalized non-integrated prt expression in all clusters at P50. TE neuron clusters are indicated by circle. d , R10D10-Gal4 co-expression with anti-Prt staining in a P50 optic lobe (n=15 neurons). Scale bar: 10 μm. e , FLEXAMP memory cassette labeling of R10D10-Gal4 in an adult optic lobe (n=28 brains) with anti-NCad staining. Scale bar: 30 μm. f, R10D10-Gal4 expression pattern in L3 optic lobe (n=15 brains), with anti-NCad, anti-Bsh and anti-Hth staining. Arrow: Bsh + , Hth - neurons labeled by R10D10-Gal4 . Scale bar: 30 μm. g-h, R10D10-Gal4 sparse expression at P30 (n=40 neurons), with anti-NCad, anti-Bsh and anti-Hth staining. Scale bars = 5 μm (g) and 15 μm (h). d/pMe: distal/proximal Medulla, Lo: Lobula, Lp: Lobula plate. I , Co-labeling of R10D10-LexA expression and bsh-Gal4 FLEXAMP memory cassette with anti-nCad staining in a P50 optic lobe (n=13 brains). Dashed ellipses: TE neurons. Scale bar: 20 μm.

    Journal: Nature

    Article Title: Neuronal diversity and convergence in a visual system developmental atlas

    doi: 10.1038/s41586-020-2879-3

    Figure Lengend Snippet: a-b, tSNE visualization of the P70 optic lobe single-cell transcriptomes, using 120 principal components calculated on the log-normalized integrated gene expression. Cells are named by the unsupervised cluster they were assigned to and colored by (a) the confidence score they received from the neural network (NN) or by (b) the log-normalized non-integrated expression of Fs (green), dimm (blue), and skl (red), which are co-expressed in TE neurons (red ellipses). c , Violin plot of log-normalized non-integrated prt expression in all clusters at P50. TE neuron clusters are indicated by circle. d , R10D10-Gal4 co-expression with anti-Prt staining in a P50 optic lobe (n=15 neurons). Scale bar: 10 μm. e , FLEXAMP memory cassette labeling of R10D10-Gal4 in an adult optic lobe (n=28 brains) with anti-NCad staining. Scale bar: 30 μm. f, R10D10-Gal4 expression pattern in L3 optic lobe (n=15 brains), with anti-NCad, anti-Bsh and anti-Hth staining. Arrow: Bsh + , Hth - neurons labeled by R10D10-Gal4 . Scale bar: 30 μm. g-h, R10D10-Gal4 sparse expression at P30 (n=40 neurons), with anti-NCad, anti-Bsh and anti-Hth staining. Scale bars = 5 μm (g) and 15 μm (h). d/pMe: distal/proximal Medulla, Lo: Lobula, Lp: Lobula plate. I , Co-labeling of R10D10-LexA expression and bsh-Gal4 FLEXAMP memory cassette with anti-nCad staining in a P50 optic lobe (n=13 brains). Dashed ellipses: TE neurons. Scale bar: 20 μm.

    Article Snippet: To produce an exhaustive catalog of neurons in the adult optic lobe ( – ), we obtained 109,743 single-cell transcriptomes (Methods) using the Chromium system (10x Genomics).

    Techniques: Gene Expression, Expressing, Staining, Labeling

    a-b, tSNE visualization of the P15 optic lobe single-cell transcriptomes, using 120 principal components calculated on the log-normalized integrated gene expression. Cells are named by the unsupervised cluster they were assigned to and colored by (a) the confidence score they received from the neural network or by (b) the log-normalized non-integrated expression of dpn (green), ase (blue), and grim (red). Circles match to those of . c, UMAP visualization of the P15 optic lobe single-cell transcriptomes, using 120 principal components calculated on the log-normalized integrated gene expression. Cells are colored by the log-normalized non-integrated expression of nerfin-1 (green), Hey (blue), and vfl (red) d, UMAP visualization of Tm3 and T1 cells (above and below the dashed line, respectively) from all stages sequenced in this study, using 25 principal components calculated on the log-normalized non-integrated gene expression. Cells are colored by their developmental stage. e, Ventral and dorsal Transient Extrinsic (TE) neurons as well as transient photoreceptors (PRs) line the edges of all optic lobe neuropils and express Follistatin ( Fs ). Moreover, TE and at least 3 other neuronal types express Wnt4 in the ventral medulla/lobula but express Wnt10 in the dorsal part of these neuropils. f, The transcriptome of neurons from the same neuronal type but produced days apart converge towards a similar transcriptomic state, which they reach by P30. Moreover, the inter-neuronal type transcriptomic diversity is highest during P40-P70.

    Journal: Nature

    Article Title: Neuronal diversity and convergence in a visual system developmental atlas

    doi: 10.1038/s41586-020-2879-3

    Figure Lengend Snippet: a-b, tSNE visualization of the P15 optic lobe single-cell transcriptomes, using 120 principal components calculated on the log-normalized integrated gene expression. Cells are named by the unsupervised cluster they were assigned to and colored by (a) the confidence score they received from the neural network or by (b) the log-normalized non-integrated expression of dpn (green), ase (blue), and grim (red). Circles match to those of . c, UMAP visualization of the P15 optic lobe single-cell transcriptomes, using 120 principal components calculated on the log-normalized integrated gene expression. Cells are colored by the log-normalized non-integrated expression of nerfin-1 (green), Hey (blue), and vfl (red) d, UMAP visualization of Tm3 and T1 cells (above and below the dashed line, respectively) from all stages sequenced in this study, using 25 principal components calculated on the log-normalized non-integrated gene expression. Cells are colored by their developmental stage. e, Ventral and dorsal Transient Extrinsic (TE) neurons as well as transient photoreceptors (PRs) line the edges of all optic lobe neuropils and express Follistatin ( Fs ). Moreover, TE and at least 3 other neuronal types express Wnt4 in the ventral medulla/lobula but express Wnt10 in the dorsal part of these neuropils. f, The transcriptome of neurons from the same neuronal type but produced days apart converge towards a similar transcriptomic state, which they reach by P30. Moreover, the inter-neuronal type transcriptomic diversity is highest during P40-P70.

    Article Snippet: To produce an exhaustive catalog of neurons in the adult optic lobe ( – ), we obtained 109,743 single-cell transcriptomes (Methods) using the Chromium system (10x Genomics).

    Techniques: Gene Expression, Expressing, Produced