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First action potential voltage change genes form a co-expression module enriched in cell adhesion genes. (a) Visualization of the Sst first AP dV/dt ephys response gene coexpression network, including correlated ion channels. Isolated first AP dV/dt genes not connected to any other genes were dropped; the full network figure is available in Supplementary Fig. S8. White nodes represent AP dV/dt genes, green nodes represent the subset of AP dV/dt genes also annotated as adhesion genes (GO:0098742), red nodes indicate AP dV/dt genes also within 5kb of a SCZ GWAS SNP, and blue nodes represent ion channels. (b) Gene set enrichment analysis of first AP dV/dt gene module from (a), with respect to all AP dV/dt genes including the isolated nodes. All enrichments shown were significant (q < 0.05) after FDR correction.
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Multimodal assays such as Patch-seq that simultaneously profile molecular and cellular phenotypes of cells enable the identification of molecular underpinnings of electrophysiological response patterns in neurons. Here we analyzed Patch-seq measurements of thousands of mouse interneurons to identify subclass-specific genes associated with different...
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... each subclass then, each ephys feature was assigned an ephys feature specificity score, defined as the percentage of correlation-ephys genes shared with fewer than five ephys features in the entire subclass. On average across the 42 ephys features in Sst neurons for example, 25% of correlation-ephys genes were only correlated to less than five ephys features, indicating a high degree of specificity of genes to specific ephys features (Fig. 2a); other subclasses showed similar levels of specificity (Supplementary Fig. S4). These results indicate different genes contribute to different ephys response features in the same subclass. ...
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... ion channels (median percentage of Sst ephys gene sets that were ion channels is 4.61%; Supplementary Table 3), we reasoned that causal ephys genes would be more likely to be also associated with ion channels. We therefore inferred a gene co-expression network of the Sst AP dV/dt genes with ion channels, and visualized their interactions (Fig. 4a). Surprisingly, of the 676 AP dV/dt genes, 93 of them form a single, connected module of strongly co-expressed genes (Fig. 4a), while most remaining genes were isolated nodes in the network (Supplementary Fig S8). Compared to the other connected gene modules from the 15 well predicted ephys features from Figure 3b, this 93-gene AP dV/dt ...
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... that causal ephys genes would be more likely to be also associated with ion channels. We therefore inferred a gene co-expression network of the Sst AP dV/dt genes with ion channels, and visualized their interactions (Fig. 4a). Surprisingly, of the 676 AP dV/dt genes, 93 of them form a single, connected module of strongly co-expressed genes (Fig. 4a), while most remaining genes were isolated nodes in the network (Supplementary Fig S8). Compared to the other connected gene modules from the 15 well predicted ephys features from Figure 3b, this 93-gene AP dV/dt gene module is enriched in cell cell adhesion, synapse signalling, neuronal morphology, membrane components, and nervous ...
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... remaining genes were isolated nodes in the network (Supplementary Fig S8). Compared to the other connected gene modules from the 15 well predicted ephys features from Figure 3b, this 93-gene AP dV/dt gene module is enriched in cell cell adhesion, synapse signalling, neuronal morphology, membrane components, and nervous system development pathways (Fig. 4b), with cell adhesion showing the strongest enrichment (p < 8.32 x 10 -3 , FDR corrected GSEA test) . Within the module includes Rgs6, a genome-wide significant SZ locus who directly regulates (GIRK)-potassium channel activation via the inhibition of GABAb receptors 62 , in addition to covarying strongly in expression with ion channels ...
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... , FDR corrected GSEA test) . Within the module includes Rgs6, a genome-wide significant SZ locus who directly regulates (GIRK)-potassium channel activation via the inhibition of GABAb receptors 62 , in addition to covarying strongly in expression with ion channels Grin2a, Grin3a and Cacna2d3. Interestingly, Rgs6 also co-varies with the gene Reln (Fig. 4a), a known SZ associated gene 56 that promotes proper neuronal positioning via adhesion as well as dendritic development 63,64 . These results suggest the AP dV/dt gene module is likely to play a functional role in ephys response, possibly through cell ...
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... heritability estimation procedure. We performed heritability analysis of multiple psychiatric and mental disorders using several different genomic annotations, including open chromatin regions for different interneuron classes 60 (Supplementary Table 4), and regions associated with our ephys response gene sets. The procedure for estimating heritability and the associated significance is the same; the only difference in the calculations is in construction of the genome annotations. ...
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... the batch effect-corrected Patch-seq gene expression matrix, subsetted to the Sst subclass. We then binarized the correlation matrix using a 99.99 th percentile threshold, followed by subsetting of the genes to the union of the ephys feature-specific ephys gene set. For our visualization of the first AP dV/dt genes together with the ion channels (Fig. 4a), we included the set of all annotated ion channel genes during the subset operation to the first AP dV/dt ephys ...
Citations
... Single cell assays have been developed to capture diverse aspects of genome regulation, including gene expression 1,2 , chromatin accessibility 3 , and methylation profiling 4,5 , among others 6,7 . These single modality assays that capture a single data type have been widely deployed on a variety of tissues and species to catalog cell types and states [8][9][10][11][12][13][14][15][16] , identify genomic features that activate at specific steps along cellular trajectories [17][18][19][20][21][22][23] , and infer regulatory networks cataloging interactions of genes, open chromatin regions or methylation sites [24][25][26] . A common step of single cell data analysis is cell state inference: the inference of a low dimensional representation of a single cell data modality, that is subsequently used for 2D data visualization 27,28 , clustering to identify discrete cell types and states, and trajectory inference tasks 29,30 . ...
... Allaway et al., E13 mouse scRNA-seq, scATAC-seq, and scMultiome dataset processing Single cell paired and unpaired multiomic datasets from the E13 mouse medial ganglionic eminence (MGE) region were obtained 65 . After cell QC steps based on quantity metrics including the number of RNA counts (300-30,000), the number of RNA features (200-6000), the number of ATAC counts (300-60,000), the number of ATAC features (300-30,000), percentage of ribosomal genes (2%-30%), nucleosome signal (<1.5) and TSS enrichment (3)(4)(5)(6)(7)(8)(9)(10)(11)(12)(13)(14)(15), performed using Seurat (v4.3.0) and Signac (v1.10.0) in R, the dataset contained 11,258 cells in the unimodal scRNA-seq dataset, 24,589 cells in the unimodal scATACseq dataset, and 5,308 cells with paired multiomic profiles. For the analysis, we adopted the strategy used by the authors of the original paper, focusing exclusively on postmitotic cells. ...
... We processed the transcriptomic data using Seurat (v4.3.0) in R and excluded the Meis2 cell type, as it has been reported as a distant branch preceding the major glutamatergic and GABAergic split 100 . We also removed the aspiny dendrite type due to their substantial difference in transcriptomics profiles from the remaining GABAergic cell population 15 . For the ephys data, we performed feature extraction on raw recording data stored in NWB files using the AllenSDK (v0. ...
Multimodal single-cell assays profile multiple sets of features in the same cells and are widely used for identifying and mapping cell states between chromatin and mRNA and linking regulatory elements to target genes. However, the high dimensionality of input features and shallow sequencing depth compared to unimodal assays pose challenges in data analysis. Here we present scPair, a multimodal single-cell data framework that overcomes these challenges by employing an implicit feature selection approach. scPair uses dual encoder-decoder structures trained on paired data to align cell states across modalities and predict features from one modality to another. We demonstrate that scPair outperforms existing methods in accuracy and execution time, and facilitates downstream tasks such as trajectory inference. We further show scPair can augment smaller multimodal datasets with larger unimodal atlases to increase statistical power to identify groups of transcription factors active during different stages of neural differentiation.
