Landscape of ribosome-engaged transcript isoforms reveals extensive neuronal-cell-class-specific alternative splicing programs

Abstract

Nervous system function relies on complex assemblies of distinct neuronal cell types that have unique anatomical and functional properties instructed by molecular programs. Alternative splicing is a key mechanism for the expansion of molecular repertoires, and protein splice isoforms shape neuronal cell surface recognition and function. However, the logic of how alternative splicing programs are arrayed across neuronal cells types is poorly understood. We systematically mapped ribosome-associated transcript isoforms in genetically defined neuron types of the mouse forebrain. Our dataset provides an extensive resource of transcript diversity across major neuron classes. We find that neuronal transcript isoform profiles reliably distinguish even closely related classes of pyramidal cells and inhibitory interneurons in the mouse hippocampus and neocortex. These highly specific alternative splicing programs selectively control synaptic proteins and intrinsic neuronal properties. Thus, transcript diversification via alternative splicing is a central mechanism for the functional specification of neuronal cell types and circuits.

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Fig. 1: Extensive alternative exon usage defines classes of forebrain neurons.
Fig. 2: Alternative splicing and alternative TSS usage drive transcript diversification in neocortical neurons.
Fig. 3: Alternative splicing programs distinguish closely related neuronal populations within the hippocampus and across brain regions.
Fig. 4: Differential splicing factors expression for cell-class-specific splicing programs in mature neurons.
Fig. 5: Alternative splicing programs are highly dedicated to the control of synaptic interactions and neuronal architecture.

Data availability

Detailed analyzed data are included as supplementary material. Raw sequencing data were deposited at GEO (accession code: GSE133291). Differential gene expression and splicing data for individual genes are provided on the freely available SpliceCode website (https://scheiffele-splice.scicore.unibas.ch). All renewable reagents and detailed protocols will be made available upon request.

Code availability

Data analysis used standard software packages, which are cited in the Methods. The FastDB database for quantitative splicing analysis is a proprietary database accessible through Genosplice Technology (http://www.genosplice.com/).

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Acknowledgements

The authors are grateful to F. Doetsch, O. Mauger and L. Xiao for constructive comments on the manuscript, and to members of the Scheiffele Lab for discussions, in particular to T. M. Nguyen for setting up the protocol for RiboTRAP purifications and C. Bornmann for expert help with fluorescence in situ hybridizations. They are grateful to S. Hrvatin and M. E. Greenberg for sharing detailed data from single-cell sequencing studies for comparison with our dataset, and to B. Rico for sharing data at an early stage of this project. They thank P. de la Grange, N. Robil and A. Jolly at Genosplice for help with analyses, and F. Ambrosetti for occasional support with coding. Part of the calculations were performed at the sciCORE (http://scicore.unibas.ch/) scientific computing center at the University of Basel, with support by the SIB (Swiss Institute of Bioinformatics). Sequencing and library preparations were performed with support from the Life Science Training Facility and the Quantitative Genomics Facility Basel. L.T. was supported by a Fellowship from the Boehringer Ingelheim PhD Fonds and the Doris Dietschy Stiftung. This work was supported by funds to P.S. from the Swiss National Science Foundation, a European Research Council Advanced Grant (SPLICECODE), and the Kanton Basel-Stadt.

Author information

This work was jointly conceived by E.F., L.T. and P.S. All wet lab procedures were performed by E.F. and L.T., and data analyses were conducted by E.F., L.T. and G.F. Website design was performed by G.F., and the manuscript was jointly written by E.F., L.T. and P.S., with editing provided by G.F.

Correspondence to Peter Scheiffele.

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The authors declare no competing interests.

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Integrated supplementary information

Supplementary Figure 1 Confirmation of selective transgene expression and selective mRNA isolation with RiboTrap approach in mice.

a, Expression pattern of HA-tagged Rpl22 protein, conditionally expressed in CamK2::Rpl22-HA, Scnn1a::Rpl22-HA, SST::Rpl22-HA, PV::Rpl22-HA, VIP::Rpl22-HA and Grik4::Rpl22-HA 3–5 weeks old mice (primary somatosensory cortex (S1, upper panel) and whole hippocampus (lower panel)). CamK2-cre-dependent expression of Rpl22-HA is mostly driven in excitatory neurons across all cortical layers and it is enriched in the CA1 region of the hippocampus. Scnn1a-cre and Grik4-cre drive Rpl22-HA expression in two regionally distinct areas, the layer 4 (L4) of the cortex and the CA3 region of the hippocampus, respectively. SST-cre, PV-cre and VIP-cre, on the other hand, determine the sparse labelling of GABAergic interneurons across the neocortical layers and in the stratum oriens and hilus of the hippocampus (in SST::Rpl22-HA mice). Cells nuclei are labelled in magenta, Rpl22-HA expressing neurons are in green. Scale bar: 200 μm. Neocortical layers and hippocampal regions are indicated. DG=dentate gyrus, CA=cornu ammonis. Experiment repeated independently 4 times, with similar results. b, Real-time qPCR for cell-type- and region-specific transcript markers confirmed the purity of immuno-isolated mRNA replicates per cell population. Transcript enrichment of the immune-isolated RNA (IP) was calculated relative to the input (total neocortical or hippocampal RNA) and was normalized to enrichments of Gapdh. N=4 qPCRs from independent IP preparations, SEM indicated. Overall, RNA isolated from inhibitory neurons show high enrichments of general GABAergic markers (Vgat, Gad67) and de-enrichments of the excitatory marker Vglut1, vice versa for immune-isolated RNA from glutamatergic neurons. RNA for all cell populations was de-enriched for the astrocytic marker gfap. Moreover, cell type specific markers (Camk2, Scnn1a, SST, PV, VIP) are relatively enriched in the corresponding preparation. For the hippocampal samples, the CA1-specific Wsf1 and CA3-specific Pvrl3 markers show enrichments in CamK2 and Grik4 samples, respectively, but the CA2- and DG-specific markers (Rgs14 and Tdo2) show lower enrichment levels. Note that endogenous CamK2 transcripts are more broadly distributed than cre-recombination in the transgenic mice. Modest enrichment values (as compared to GABAergic markers) in IPs are a consequence of the broad transcript expression.

Supplementary Figure 2 FastQC analysis of RNA-sequencing data.

For each biological replicate of each sample, the following parameters are indicated: total number of reads, % of reads uniquely mapped to the reference genome, number of detected genes, % ribosomal contamination, % of mRNA representation and % of reads mapped to exon-exon junctions. All samples show highly similar values across biological replicates, as well as across samples, suggesting a high consistency and homogeneity of the RNA-seq data.

Supplementary Figure 3 Quality controls confirmed the purity of RiboTRAP samples.

a, Coverage plot indicating the percentage of read bases at a given position of the transcript. All biological replicates of all samples used for analysis show excellent 5’ to 3’ coverage across the transcript length. b, Heatmaps representing the expression of cell type-specific marker genes identified by single cell sequencing in Hrvatin et al., 20181. Left panel: marker genes defined by Hrvatin et al. show appropriate enrichments in the RiboTRAP neocortical samples generated in the present study. Right panel: average expression of the same markers (order as in left panel) across pools of single cells belonging to the indicated classes (data from Hrvatin et al., 2018. ExcL4=glutamatergic L4-specific, Exc=pan-neocortical glutamatergic, Int_Vip=VIP-positive, Int_PV=PV-positive, Int_SST=SST-positive). Overall, previously identified cell type-specific marker genes show very similar relative enrichments in single-cell and RiboTrap datasets. Significant differential gene expression for the RiboTrap (our study) was determined using the Ward test and the Benjamini Hochberg method for p-value adjustment. c, Boxplot representing the distribution of log2(FPKM) values in either CamK2 (left panel) or Grik4 (right panel) samples for all genes detected, and for previously described DG-, CA1-, CA3- and astrocytic-specific markers (see Supplementary Table 1 for the complete marker lists). N=4 biologically independent samples; FPKM expression values for cell-class specific markers were averaged. Minima and maxima correspond to the 25th and 75th percentile, respectively, center correspond to median expression value. In both CamK2 and Grik4 samples, overall expression of DG- and astrocytic-specific markers are represented below the median expression value of all genes (indicated in the graph as red dotted line). By contrast, CA1- and CA3-specific markers expression is higher in CamK2 and Grik4 samples, respectively. d, Relative enrichment of previously described CA1- and CA3-specific marker expression in Grik4 compared to CamK2 samples (left panel) and in CamK2 compared to Grik4 samples (right panel). N=4 biologically independent samples; FPKM expression values for cell-class specific markers were averaged, SEM is indicated. Grik4 samples show strong relative enrichment of CA3-specific markers and strong de-enrichments of CA1-specific markers expression, vice versa for CamK2 samples.

Supplementary Figure 4 Cell population-specific markers identified show highly selective enrichments.

Heatmap of transcripts highly enriched in a specific cell population compared to all neocortical and hippocampal neuron populations (log2(FC) ≥ 3, P ≤ 0.01, Ward test and Benjamini-Hochberg method for p-value adjustment. Base mean includes all samples). Cell class-specific markers show high enrichments across all biological replicates of one population and strong de-enrichments in all the others. Well-known and new cell-population markers were identified (see Supplementary Table 1 for a complete list of highly enriched genes).

Supplementary Figure 5 Alternative splicing analysis pipeline.

a, Cartoon illustrating transcript annotation in FAST DB. For every gene present in the Mouse FAST DB v2016_1 database2, all annotated transcripts are listed (upper panel). For every transcript, exons are defined as constitutive (if present in more than 75% of the transcripts generated from a given gene, for “GeneA” in red), semi-constitutive (if present in between 50% and 75% of all transcripts, for “GeneA” in orange) or alternative (if included in less than 50% of transcripts, for “GeneA” in yellow). Moreover, for every gene, all previously described splicing patterns are annotated (lower panel). b, In the Exon analysis, for every exon expressed (see methods for definition of exon expression), a splicing index (SIEXON) was determined by calculating the ratio of the read density on a given exon and the read density on all constitutive exons of the gene. The SIEXON indicates the rate of inclusion of each expressed exon in every expressed gene. A fold-change is then calculated by comparing the average SIEXON of the four replicates of a given sample (condition A) and the average SIEXON of condition B (that is, average SIEXON of another sample, in the case of pairwise comparisons, or average SIEXON across all neocortical samples, including sample of condition A). c, In the Pattern analysis, for every pattern annotated in the database, the read density was quantified. For every condition, read density of the two possible patterns was normalized by the density of constitutive regions of the patterns (in the example, e1 and e3) and a fold-change was calculated by comparing the normalized pattern read density in the two conditions A and B.

Supplementary Figure 6 Semi-quantitative PCR validation of predicted alternative splicing events.

a, Analysis of alternative splicing events in total neocortex (input) and CamK2, Scnn1a, SST, PV, VIP neocortical immuno-isolated RNAs. Flanking primers were used to amplify exons involved in the events found to be differentially regulated in neocortical samples by RT-PCR. The names of genes and schematic representation of the exons amplified are indicated. For each sample, three PCR reactions were performed and band intensity was quantified. Representative images are shown. Mean percentage of exon inclusion or exclusion in the three replicates is indicated below. For the 22 predicted differentially regulated events tested, 20 were experimentally validated (>90%, 8 not shown), confirming the high confidence of sequencing predictions. For full gel images see Supplementary Figure 12. b, Quantification of PCR assessing the relative usage of alternatively spliced exons in neocortical samples. Single data points represent the percentage of band intensity of the exon of interest relative to the sum intensity of both bands (N=3 PCRs from three independent immune-isolated RNA samples, SEM is indicated). Gene names and exons amplified are indicated.

Supplementary Figure 7 Differentially used exons are detected over a wide range of gene expression levels.

Correlation plots showing the relation between differential usage of each detected exon in neocortical populations and the expression of the corresponding gene. The log2 fold-change of splicing index obtained from Exon analysis (log2(FC)_SI, base mean includes all neocortical samples) was compared to the log2(FPKM) expression value of the gene including a given exon. N=4 biologically independent samples; FPKM expression values for cell-class specific markers were averaged. Exons found to be differentially included in the cell population indicated are highlighted in red (log2(FC) ≥ 1 and ≤ -1, P ≤ 0.01, unpaired Student’s t-test). Gray dotted lines represent the 25th (lower) and 75th (upper) percentile of log2(FPKM) values of all expressed genes. On the bottom left of the graph, Pearson’s correlation value (R) is indicated. We do not observe a correlation of gene expression levels and differential exon usage.

Supplementary Figure 8 Differential Transcription start site and alternative last exon usage across neocortical cell populations.

a, b, Heatmaps of SI values obtained from Exon analysis (see methods for details) for differentially regulated exons in neocortical cell populations (log2(FC) ≥ 1 and ≤ -1, P ≤ 0.01, unpaired Student’s t-test; base mean includes all neocortical samples). Panel (a) shows exons contributing to alternative transcription start sites (TSSs). Panel (b) shows alternative last exons (ALE). Amongst the 1111 differentially regulated TSS events, the two glutamatergic samples (CamK2 and Scnn1a) share 124 common events. Within the GABAergic populations, PV neurons share 50 and 45 common TSS events with SST and VIP samples, respectively, while SST sample have 34 common TSS events with VIP neurons. Amongst the 867 differentially regulated ALE events, the two glutamatergic samples (CamK2 and Scnn1a) share 80 common events. On the other hand, within the GABAergic populations, PV neurons share 42 and 38 common TSS events with SST and VIP samples, respectively, while SST sample have 19 common TSS events with VIP neurons.

Supplementary Figure 9 Pattern analysis in pairwise hippocampal and across brain regions comparisons reveals similar percentages of alternative TSSs and AS events compared to neocortical populations.

a, Pie charts indicating the relative percentage of alternative transcription start site (TSSs) and alternative splicing events out of the differentially regulated events identified by the Pattern analysis (log2(FC) ≥ 1, P ≤ 0.01) in the pairwise hippocampal comparisons (CamK2Hc vs Grik4, Grik4 vs SSTHc, SSTHc vs CamK2Hc) and in the pairwise comparisons across brain regions (CamK2Hc vs Scnn1a, SSTCx vs SSTHc). Total number of differentially regulated patterns are: 321 for CamK2Hc vs Grik4, 1126 for Grik4 vs SSTHc, 1131 for SSTHc vs CamK2Hc, 1054 for CamK2Hc vs Scnn1a and 98 for SSTCx vs SSTHc. b, Histogram representing the relative percentage of differentially regulated alternative splicing event categories in the hippocampal and across brain regions comparisons. The distinct pattern categories (mutually exclusive exon, cassette exon, intron retention, alternative 5’ and 3’ donor and acceptor site, alternative last exon, complex) are indicated in the legend. Total number of differentially regulated splicing patterns are: 146 for CamK2Hc vs Grik4, 673 for Grik4 vs SSTHc, 667 for SSTHc vs CamK2Hc, 722 for CamK2Hc vs Scnn1a and 30 for SSTCx vs SSTHc.

Supplementary Figure 10 Validation of cell class-specific expression of splicing factors in neocortical and hippocampal neurons.

a,b, Validation of cell population-specific expression of candidate splicing factors in neocortical (panel a) and hippocampal (panel b) mouse brain by fluorescent in situ hybridization. In situ probes assessing transcripts levels of Elavl2, Rbfox3, Ptbp3 and Celf4 were used in combination with cell population-specific marker probes (CamK2 for cortical and for CA1- and CA3-specific glutamatergic neurons, Rorb for layer 4-specific glutamatergic neurons, PV, VIP and SST for parvalbumin-, vasointestinal peptide- and somatostatin-positive interneurons, respectively). a, Left panel: regions of interest (ROI) identify Rorb- or PV-positive neurons. Marker signal is in green, Elavl2 and Rbfox3 in black (red in Merge), DAPI in magenta. Images were taken from primary somatosensory cortex (S1) of P25 mice. b, Left panel: ROIs (regions of interest) identify CamK2- or SST-positive neurons. Marker signal is in green, Elavl2 and Rbfox3 in black (red in Merge), DAPI in magenta. Images were taken from CA1 or CA3 regions (for CA1 and CA3 pyramidal neurons, respectively) or from the stratum oriens (for SST-positive neurons) of the hippocampus of P25 mice. s.r.=stratum radiatum; s.o.=stratum oriens. Right panels: Quantification of the number of mRNA dots/100 μm2 in the ROIs positive for the marker probe. Single data points represent the mean number of dots co-localizing with all the marker-positive cells from a single replicate (N=3 animals). SEM of the three independently performed in-situ hybridizations are represented by circles, total number of cells counted from all the replicates are indicated in the plot. Note that Rbfox3 encodes for the widely used neuronal marker NeuN. Scale bar is 20μm.

Supplementary Figure 11 Gene expression programs show distinct enrichments of GO categories as compared to alternative splicing.

a,b, Heatmaps representing fold enrichment of Gene Ontology (GO) terms for transcripts differentially expressed identified by the Panther Classification System. Terms listed are significantly enriched in at least one neocortical population (panel a), hippocampal comparison (b, left panel) or across brain regions comparison (b, right panel; see methods for details of significance). Fields for the statistically significant enrichments (P ≤ 0.05 determined by Fisher’s exact test with Benjamini-Hochberg false discovery rate correction) are highlighted by a dashed outline. See Supplementary Table 7 for the raw output of the GO analysis. c, Density plot showing the distribution of genes according to their number of exons. Gene lists were extracted from GO categories enriched for differentially spliced genes (ion channels, in black, synaptic vesicle, in red, and postsynaptic membrane, in light-blue), and from GO categories not significantly enriched (spliceosomal complex, in yellow, early endosome, in green, and chromosomal region, in blue). Chosen GO categories had similar numbers of genes per category (number of genes are indicated next to the name of the category). Overall, genes belonging to enriched or non-enriched categories for alternative splicing do not show major differences in the number of exons per gene.

Supplementary Figure 12 Scans of validation PCRs corresponding to Supplementary Figure 6.

Full scans of the 12 example PCR gels displayed in Supplementary Figure 6a for sequencing validation. Asterisks indicate primer dimer bands below the molecular weight of 100bp which were observed at variable rates throughout the independent experiments performed for three biological replicates. Appropriate sizes of all PCR products were confirmed in initial experiments. In cases where the signal intensity of the molecular weight ladder would interfere with the signal of the PCR reaction, the ladder was not loaded. However, band sizes were confirmed based on pattern and migration properties (for example relative to loading dyes). In these cases, approximate sizes of markers have been added post-hoc.

Supplementary information

Supplementary Figs. 1–12

Reporting Summary

Supplementary Table 1

Gene expression data in cortical and hippocampal samples and markers. Sheet 1 (NF_counts_genes): Gene expression values for all detected genes (see Methods for details of cut-offs for gene expression) across all neocortical and hippocampal samples (n = 4 biologically independent replicates). Sheet 2 (genes_cortex_FC_p-val): log2 fold-change (log2(FC)), standard error (log2(FC_SE)), P value (Pval) and adjusted P value (adjP) for genes expressed in all neocortical cell classes. Base mean includes all neocortical samples. Statistical test used to determine statistical significance of gene expression between different cell classes was the Ward test with Benjamini–Hochberg P value adjustment. Sheet 3, 4, 5 (genes_CamK2vsGrik4_FC_p-val, genes_Grik4vsSST_FC_p-val, genes_SSTvsCamK2_FC_p-val): log2 fold-change (log2(FC)), standard error (log2(FC_SE)), P value (Pval) and adjusted P value (adjP) for genes expressed in each hippocampal pairwise comparison. Statistical test used to determine statistical significance of gene expression between different cell classes was the Ward test with Benjamini–Hochberg P value adjustment. Sheet 6 (genes_cortex+hippo_FC_p-val): log2 fold-change (log2(FC)), standard error (log2(FC_SE)), P value (Pval) and adjusted P value (adjP) for genes expressed in neocortical and hippocampal samples. Base mean includes all neocortical and hippocampal samples (Ward test with Benjamini–Hochberg P value adjustment). Sheet 7 (Markers_genes_cortex and hippo): list of marker genes identified in neocortical and hippocampal samples (log2(FC) ≥ 3, adjP ≤ 0.01. Base mean includes all neocortical and hippocampal samples). Gene symbol, the corresponding marker class, log2(FC) and adjP are indicated (Ward test with Benjamini–Hochberg P value adjustment). Sheet 8 (Hrvatin et al., 2018 markers): List of cell type-specific marker genes identified by single cell sequencing in Hrvatin et al., 2018. Gene symbol, the corresponding marker class, log2(FC) and adjP are indicated. Base mean includes all neocortical samples. Sheet 9 (CA1_CA3_DG_astro markers): list of previously described DG-, CA1-, CA3- and astrocytic-specific markers, with corresponding references.

Supplementary Table 2

Alternative splicing analysis (Exon and Pattern) in neocortical samples. Sheet 1 (SI_EXON_neocortex): splicing index (SI) values identified by Exon analysis for all exons detected in all neocortical samples (n = 4 biologically independent replicates; see Methods for details of cut-offs for exon expression), unpaired Student’s t-test. Exon number, relative position in the gene and chromosomal coordinates are indicated. When known, the pattern associated to the exon is indicated (0 = FALSE, 1 = TRUE). Sheet 2 (FC_SI_EXON_neocortex): log2(FC) and P values of splicing index (SI) identified by Exon analysis for all exons detected in neocortical samples (n = 4 biologically independent replicates; see Methods for details of cut-offs for exon expression), unpaired Student’s t-test. Base mean includes all neocortical samples. Exon number, relative position in the gene and chromosomal coordinates are indicated. When known, the pattern associated to the exon is indicated (0 = FALSE, 1 = TRUE). Sheet 3 (FC_SI_PATTERN_neocortex): log2(FC) and P values of SI for events identified by the Pattern analysis in neocortical samples. Base mean includes all neocortical samples (n = 4 biologically independent replicates; see Methods for details of cut-offs for exon expression), unpaired Student’s t-test. For every pattern detected, the exons involved, their up- or down-regulation in each cell population and the chromosomal coordinates are indicated. When the event involved more than one exons, the chromosomal coordinates of the second exon are also indicated. ALE = alternative last exon; ALT_TSS = alternative transcription start site; 3PACCEPTOR = alternative 3′ acceptor site; 5PDONOR = alternative 5′ donor site; CASSETTE = cassette exon; COMPLEX = complex event; IED = internal exon deletion; INT_RET = intron retention; MX = mutually exclusive exon.

Supplementary Table 3

Alternative splicing analysis (Exon and Pattern) in hippocampal pairwise comparisons. Sheet 1 (SI_EXON_hippocampus): splicing index (SI) values identified by Exon analysis for all exons detected in all hippocampal samples (n = 4 biologically independent replicates), unpaired Student’s t-test. Exon number, relative position in the gene and in the chromosome are indicated. When known, the pattern associated to the exon is indicated (0 = FALSE, 1 = TRUE). Sheet 2 (FC_SI_EXON_hippo_pairwise): log2(FC) and P values of SI identified by Exon analysis in hippocampal pairwise comparisons (Camk2 vs Grik4, Grik4 vs Sst, Sst vs Camk2; n = 4 biologically independent replicates; unpaired Student’s t-test.). Exon number, relative position in the gene and in the chromosome are indicated. When known, the pattern associated to the exon is indicated (0 = FALSE, 1 = TRUE). Sheet 3, 4, 5 (FC_SI_PATTERN_CamK2vsGrik4, sheet 2: FC_SI_PATTERN_Grik4vsSST, sheet 3: FC_SI_PATTERN_SSTvsCamK2): log2(FC) and P value of events identified by the Pattern analysis in pairwise comparisons between hippocampal samples (n = 4 biologically independent replicates; unpaired Student’s t-test). For every pattern detected, the exons involved, their up- or down-regulation and the chromosomal coordinates are indicated. When the event involved one than more exons, also the chromosomal coordinates of the second exon are indicated. ALE = alternative last exon; ALT_TSS = alternative transcription start site; 3PACCEPTOR = alternative 3′ acceptor site; 5PDONOR = alternative 5′ donor site; CASSETTE = cassette exon; COMPLEX = complex event; IED = internal exon deletion; INT_RET = intron retention; MX = mutually exclusive exon.

Supplementary Table 4

Gene expression and alternative splicing analysis (Exon and Pattern) in pairwise comparisons across brain regions. Sheet 1, 4, 7 (genes_SSTCxvsSSTHc, genes_CamK2HcvsScnn1a, genes_CamK2HcvsCamK2Cx): log2 fold-change (log2(FC)), standard error (log2(FC_SE)), P value (Pval) and adjusted P value (adjP) for genes expressed in pairwise comparisons across brain regions (n = 4 biologically independent replicates, statistical test used was the Ward test with Benjamini–Hochberg P value adjustment). Sheet 2, 5, 8 (FC_SI_EXON_ SSTCxvsSSTHc, FC_SI_EXON_ CamK2HcvsScnn1a, FC_SI_EXON_ CamK2HcvsCamK2Cx): log2(FC) and P values of SI identified by Exon analysis in pairwise comparisons across brain regions (n = 4 biologically independent replicates; unpaired Student’s t-test). Exon number, relative position in the gene and in the chromosome are indicated. When known, the pattern associated to the exon is indicated (0 = FALSE, 1 = TRUE). Sheet 3, 6, 9 (FC_SI_PATTERN_SSTCxvsSSTHc, FC_SI_PATTERN_ CamK2HcvsScnn1a, FC_SI_PATTERN_CamK2HcvsCamK2Cx): log2(FC) and P value of events identified by the Pattern analysis in pairwise comparisons across brain regions (n = 4 biologically independent replicates; unpaired Student’s t-test). For every pattern detected, the exons involved, their up- or down-regulation and the chromosomal coordinates are indicated. When the event involved one than more exons, also the chromosomal coordinates of the second exon are indicated. ALE = alternative last exon; ALT_TSS = alternative transcription start site; 3PACCEPTOR = alternative 3′ acceptor site; 5PDONOR = alternative 5′ donor site; CASSETTE = cassette exon; COMPLEX = complex event; IED = internal exon deletion; INT_RET = intron retention; MX = mutually exclusive exon.

Supplementary Table 5

Summary Table of differentially expressed genes and regulated alternative events for all comparisons. Three types of comparisons are indicated: (1) neocortical samples compared to the base mean of neocortical samples only; (2) neocortical and hippocampal samples compared to the base mean of all samples; (3) pairwise comparisons (n = 4 biologically independent replicates). Numbers for differentially expressed genes (Pval ≤ 0.05, –0.6 ≤ log2(FC) ≥ 0.6, (Ward test with Benjamini–Hochberg P value adjustment) or DR exons (Pval ≤ 0.01, 1 ≤ log2(FC) ≥ 1, unpaired Student’s t-test) and DR patterns (Pval ≤0.01, log2(FC) ≥ 1, unpaired Student’s t-test) are depicted. Furthermore, numbers specifically regulated by alternative splicing (AS) or TSS events identified by either Exon or Pattern analysis are shown.

Supplementary Table 6

Splicing factors expression in neocortical and hippocampal samples. Sheet 1 (Sp.factors_Cx+Hc_FC_P-val): log2 fold-change (log2(FC)) and adjusted P value (adjP) for a hand-curated list of high-confidence splicing factors (n = 4 biologically independent replicates, Ward test with Benjamini–Hochberg P value adjustment). Base mean includes all neocortical and hippocampal samples. Sheet 2 (Statistics_FISH_sp.factors): statistical tests of fluorescent in situ results in Supplementary Fig. 9. N = 3 independent experiments, one way-ANOVA with Tukey’s multiple comparisons test was performed for each comparison and for each splicing factor probe (Elavl2, Ptbp3, Rbfox3 and Celf4), both in the neocortex and hippocampus.

Supplementary Table 7

Gene Ontology analysis. Lists of all terms from the Gene Ontology analysis for transcripts regulated at gene or alternative splicing level for neocortical cell classes (sheet 1: GO_neocortex), for hippocampal pairwise comparisons (sheet 2: GO_hippocampus) and for pairwise comparisons across brain regions (sheet 3 and 4: GO_CamK2HcvsScnn1a, GO_SSTCxvsSSTHc). N = 4 biologically independent replicates. Information on the number of background genes, number of genes detected in the category, Fold-enrichment and false discovery rate (Fisher’s exact test with Benjamini–Hochberg false discovery rate correction) are indicated.

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