Deep proteomic network analysis of Alzheimer’s disease brain reveals alterations in RNA binding proteins and RNA splicing associated with disease

Fresh frozen brain tissue blocks from dorsolateral prefrontal cortex (Brodmann area 9) were used for analysis, as described previously [5]. Frozen aliquots from the same brain homogenate were used for LFQ and TMT analysis. Symptomatic AD (n = 20), asymptomatic AD (AsymAD) (n = 14), and control (n = 13) cases were processed and analyzed. In addition to these n = 47 cases, mild cognitive impairment (MCI) cases (n = 11) were homogenized separately on a different day and included in the batched TMT-MS design, but were later excluded from the analysis due to a preparation batch effect that was refractory to post-hoc correction. Sample information is given in Additional file 1: Table S1 and Additional file 2: Table S2. The TMT-MS experimental design is shown in Additional file 3: Table S3.

Each tissue piece (approx. 100 mg wet weight) was homogenized in 500 μL of urea lysis buffer (8 M urea, 100 mM NaH₂PO₄, pH 8.5), supplemented with 5 μL (100× stock) HALT protease and phosphatase inhibitor cocktail (Pierce) using a Bullet Blender (Next Advance) and 750 mg of steel beads (Next Advance). Protein supernatants were then transferred to new 1.5 mL Eppendorf tube and sonicated (Sonic Dismembrator, Fisher Scientific) 3 times for 5 s with 15 s intervals of rest at 30% amplitude. Protein concentration was determined by the bicinchoninic acid (BCA) method, and samples were frozen in aliquots at − 80 °C. Protein integrity was checked by one-dimensional SDS-PAGE (Additional file 8: Figure S1). The MCI case samples were homogenized on a later day than the control, AsymAD, and AD cases, but digestion prior to TMT labeling was performed at the same time.

Protein homogenates (100 μg) were treated with 1 mM dithiothreitol (DTT) at 25 °C for 30 min, followed by 5 mM iodoacetimide (IAA) at 25 °C for 30 min in the dark. Protein was digested with 1:100 (w/w) lysyl endopeptidase (Wako) at 25 °C overnight. Resulting peptides were desalted with a Sep-Pak C18 column (Waters). All samples were dried down completely using a Savant SpeedVac (ThermoFisher Scientific). In addition to the 58 case samples, a global internal standard (GIS) mixture of case sample homogenates (n = 60, 30 control and 30 AD) taken from multiple different patient cohorts was generated by mixing each sample equally by protein amount prior to TMT labeling on a designated reporter channel. TMT labeling was performed per the manufacturer’s protocol and as previously described [10]. Briefly, the reagents were equilibrated to room temperature. Dried peptide samples (100 μg each) were resuspended in 100 μl of 100 mM TEAB buffer (supplied with the kit). Anhydrous acetonitrile (ACN) (41 μl) was added to each labeling reagent tube and the peptide solutions were transferred into their respective channel tubes. The reaction was incubated for 1 h and quenched for 15 min afterward with 8 μl of 5% hydroxylamine. Samples were combined according to the batch design shown in Additional file 3: Table S3, and dried down to 100 μl to remove ACN. The combined samples were then desalted using a Sep-Pak C18 column (Waters) and dried down to approximately 5 μl. The labeled peptide sample batches were each further diluted with 100 μl of 90% ACN and 0.1% acetic acid (buffer A) and loaded onto an offline electrostatic repulsion–hydrophilic interaction chromatography (ERLIC) fractionation HPLC system [10, 13]. A total of 40 fractions were collected over a 40-min gradient from 0 to 28% Buffer B (30% ACN and 0.1% formic acid). The 40 fractions were combined down to 20 and dried down to completeness.

Dried peptide fractions were resuspended in 30 μl of peptide loading buffer (0.1% formic acid, 0.03% trifluoroacetic acid, 1% acetonitrile). Peptide mixtures (2 μl) were separated on a self-packed C18 (1.9 μm Dr. Maisch, Germany) fused silica column (25 cm × 75 μM internal diameter; New Objective) by a Dionex Ultimate 3000 RSLCNano and monitored on a Fusion mass spectrometer (ThermoFisher Scientific). Elution was performed over a 140-min gradient at a rate of 300 nl/min with buffer B ranging from 3 to 80% (buffer A: 0.1% formic acid in water, buffer B: 0.1% formic acid in acetonitrile). The mass spectrometer was programmed to collect at the top speed for 3 s cycles in synchronous precursor selection (SPS)-MS3 mode [10, 14]. The MS scans (380–1500 m/z range, 200,000 AGC, 50 ms maximum ion time) were collected at a resolution of 120,000 at m/z 200 in profile mode. CID MS/MS spectra (2 m/z isolation width, 35% collision energy, 10,000 AGC target, 35 ms maximum ion time) were detected in the ion trap. HCD MS/MS/MS spectra (2 m/z isolation width, 65% collision energy, 100,000 AGC target, 120 ms maximum ion time) of the top 5 MS/MS product ions were collected in the Orbitrap at a resolution of 60000 [10]. Dynamic exclusion was set to exclude previous sequenced precursor ions for 30 s within a 10 ppm window. Precursor ions with + 1 and + 8 or higher charge states were excluded from sequencing.

MS/MS spectra were searched against a Uniprot human database (downloaded on 04/15/2015 with 90,411 target sequences) with Proteome Discoverer 2.1 (ThermoFisher Scientific). The database included all Swiss-Prot-curated (canonical) plus TrEMBL (unreviewed) sequences, totaling 90,411 FASTA sequence entries. Methionine oxidation (+ 15.9949 Da), asparagine, and glutamine deamidation (+ 0.9840 Da) and protein N-terminal acetylation (+ 42.0106 Da) were variable modifications (up to 3 allowed per peptide); static modifications included cysteine carbamidomethyl (+ 57.0215 Da), peptide N-terminus TMT (+ 229.16293 Da), and lysine TMT (+ 229.16293 Da). Only peptides resulting from LysC digestion were considered, with up to two miscleavages, in the database search. A precursor mass tolerance of ±20 ppm and a fragment mass tolerance of 0.6 Da were applied. Spectra matches were filtered by Percolator [15] to a peptide-spectrum match false discovery rate of < 1%. Strict parsimony was observed for peptide to protein matching, and only razor and unique peptides were used for abundance calculations. Log₂ ratio of sample over the GIS was used for comparison across all samples.

GIS mixture (MS³ TMT reporter channel m/z 126) provided as Proteome Discoverer 2.1 script output was checked for extreme outlier values of log₂(0.01) and log₂(100), i.e. ±6.64; these values were excluded from analysis. Furthermore, proteins with more than 4 unquantifiable batches (out of a total of 8 batches) due to 0 or NA value for the GIS channel 126 reporter Proteome Discoverer 2.1-normalized value (pre-ratio calculation) were excluded from consideration. Finally, proteins with more than 23 missing log₂(ratio) values were excluded from analysis, and then 11 MCI cases were dropped, leaving a matrix of n = 47 control, AsymAD, and AD cases with no more than 23 missing values (< 50%) per protein measurement, for a total of 6532 proteins. Amyloid-β log₂(ratio) represented by TMT peptide level quantitation of the APP LVFFAEDVGSNK peptide was added to the final 6533 × 47 protein abundance matrix.

The covariate-unregressed, normalized abundance matrix described above was collapsed to average protein abundance measurements for unique gene symbols (n = 5,839) using WGCNA::collapseRows() function [16]. Two thousand one hundred thirty two cell type marker gene symbols from pure cell types of mouse brain [17] (referred to as the Sharma dataset) which we previously defined via thresholding used for cell type enrichment analyses of human proteome coexpression modules [5, 17] were converted from mouse to human gene symbols using biomaRt R interface to the public Ensembl datamart as of July 2017 [18]. From this set, 895 gene symbols representing collapsed and averaged protein abundances with no missing quantification values across the 47 BLSA case tissue samples overlapped the Sharma quantitative dataset. The overlapping marker measurements from Sharma purified brain cell types and our BLSA middle frontal gyrus samples were input into the DSA v1.0 R package [19] and estimated weights were found using the DSA::EstimateWeight() function.

A second, two-pass regression scheme was performed by first considering DSA estimated cell type weight for the four Sharma dataset brain cell types (microglia, astrocytes, neurons, and oligodendroglia) as four sets of variables for regression. Following normalization of cell type abundance variation across the samples, the prior age, sex, and PMI regression scheme was used to remove these covariate effects. Only the first pass regressed protein abundance matrix was used for WPCNA. Importantly, missing values did not require imputation for bootstrap covariate regression.

Threshold power Beta for reduction of false positive correlations (i.e. the beneficial effect of enforcing scale free topology) was sampled in increments of 0.5 and selected as the lowest power at which scale free topology R² was approximately 0.80, or in the case of the cell type weight-regressed network, the power at which a horizontal asymptote (plateau) was nearly approached, near a scale free topology R² of (0.80). Other parameters were selected as previously optimized for protein abundance networks [5]. Thus, for the signed network build on protein abundances after naïve age, sex, and PMI regression, parameters were input into the WGCNA::blockwiseModules() function as follows: Beta (power) 8.0, mergeCutHeight 0.07, pamStage TRUE, pamRespectsDendro TRUE, reassignThreshold p < 0.05, deepSplit 2, minModuleSize 17, replaceMissing TRUE, corType bicor, maxBlockSize greater than the total number of proteins (6,533), and TOMDenominator mean.

GO analysis for module membership was performed using GO-Elite [20] with the background set to all 5,839 gene symbols quantified in this study. Gene lists per module were subjected to Fisher exact overlap test in the python command line version of GO-Elite v1.2.5 for species setting Hs against the current (downloaded June 2017) annotation database for Biological Process, Molecular Function, and Cellular Component terms. Cytoscape with the EnrichmentMAP app [21] was used to visualize ontology representation, overlap, and relatedness.

Differential expression analysis was performed as previously described [5]. Briefly, differentially expressed proteins were found using one-way ANOVA followed by Tukey’s comparison post hoc (p value < 0.05). Volcano plots were generated with the ggplot2 package in R. Custom R scripts were used to visualize overlap of differentially expressed targets with WPCNA modules.

MAGMA [22] for p value calculation of GWAS target enrichment in WPCNA modules was performed as previously described [5]. Hypergeometric overlap significance tests, namely one-tailed Fisher exact and two-tailed overrepresentation analysis, were performed as previously described [5].

The GSNAP algorithm with novel splicing flag (-N) on [23] was used to realign raw short paired end RNA-Seq reads of 3 control and 3 AD cases from the University of Kentucky brain bank originally published in Bai, et al. [24] to the GRCh37 human genome build with contigs and the 16,569 nucleotide (nt) mitochondrial genome. Then all exon-exon junctions represented by 2 or more gapped reads across the 6 case sample cDNA libraries, with a minimum exonic overlap of 4 nt, were summarized using the R spliceSites bioconductor package. A custom R script and Excel formulas for string manipulation were used to extract LysC [K|P] peptides spanning exon-exon junctions (both with and without miscleavage at proline). All junction-spanning peptides considered were ones that had alternative events represented by other gapped reads that shared a left (5′) or right (3′) end with another set of gapped reads, and not “singleton” or brain constitutive exon-exon junctions. Peptides from different genomic sites that were 100% homologous to the junction-spanning peptides were considered duplicates and were removed from consideration. The resulting list of annotated alternative exon-exon junction-spanning peptides (N > 58,319) detected in brain transcriptome were concatenated as FASTA entries to the April 2015 human Uniprot database, and then Proteome Discoverer 2.1 was used to search and quantify peptide reporter channels across all 8 batches of TMT data with parameters otherwise as described above for the initial search. Peptide summary output for each of the 8 batches was opened in Excel, and all peptides annotated in the expanded human database as brain-specific alternative exon-exon junction peptides—including different modified forms of the same fully LysC digested peptides—were found and summed using the Excel sumif() function. These unified quantitations were performed over the different post-translationally modified states of the same peptide (e.g., N-terminal acetylation, or N/Q deamidation, or M oxidation) for all alternative exon-exon junction peptides in the peptide-level summary output for each of the 8 10-plex batches of ERLIC fractions. Quantitations of within-batch normalized abundances were then scaled across batches to set the average of all GIS measurements within batch to be identical across batches. The scaled, normalized, summed peptide abundances were log2-transformed; 9 negative values (< 1 before log2 transformation) were removed from the matrix. Regression for age, sex, and PMI covariation was performed in R on all log2 transformed values except for 781 that could not be regressed due to a high number of missing values. After regression, ANOVA with Tukey post hoc correction was performed on both regressed and unregressed values. The regressed alternative exon-exon junction peptide abundances were matched to the 50 WPCNA eigenproteins by calculating kME (correlation to module eigenprotein) for each peptide and assigning the peptide to the module with the highest correlation. For the purposes of avoiding spurious correlations, no more than 25 out of 47 missing values were allowed for any peptide. Venn and volcano plots were produced in R using vennDiagram, ggplot2, and/or plotly R packages.

In our previous analysis of dorsolateral prefrontal cortex (DLPFC) brain tissue from AD, asymptomatic AD (AsymAD), and control cases from the Baltimore Longitudinal Study of Aging (BLSA) [6] cohort, we were able to identify 3,069 proteins with 10 % or less missing values across 47 DLPFC brain samples (excluding precuneus samples) using “single-shot” one-dimensional online reverse-phase HPLC fractionation and label-free quantitation (LFQ) [5]. This represented a reduction from 5138 total proteins identified across all DLPFC samples due to missing peptide quantitative values in greater than 10% of the samples. In order to address the limitation of LFQ by data-dependent LC-MS/MS when analyzing protein levels across multiple samples, we reprocessed and reanalyzed the same DLPFC homogenates using a multiplex isobaric tandem mass tag (TMT) labeling approach and synchronous precursor selection-based mass spectrometry (SPS-MS3) quantitation on a tribrid mass spectrometer, coupled with orthogonal offline prefractionation [8, 10]. As part of the new analysis approach, we also relaxed the data inclusion criteria to require missing values in < 50% rather than < 10% of the samples, given that the WGCNA algorithm for coexpression network analysis well-tolerates missing values up to 50%. We subsequently refer to this quantitation and analysis approach as our “TMT pipeline.” Using the TMT pipeline, we were able to identify and quantify 6,533 proteins, compared to 3,069 proteins using the previous single-shot LFQ strategy. The large majority of the increase in protein coverage was due to the superior quantitation provided by TMT labeling and prefractionation rather than the relaxed missing values tolerance threshold (Additional file 9: Figure S2). To validate that protein quantitation was similar using the two different quantitation approaches, we compared the relative levels of the amyloid-β (Aβ)17–28 peptide in each sample quantified by LFQ and TMT. The Aβ17–28 peptide is a proteolytic fragment of Aβ generated by both trypsin and LysC enzymatic digestion of the full-length Aβ peptide, and therefore represents a peptide with a very large change in abundance across the sample cohort due to aggregation of Aβ into amyloid plaques in AsymAD and AD cases [5]. An illustration of Aβ17–28 quantitation by TMT is shown in Additional file 10: Figure S3A, with correlation of this Aβ peptide measurement to cerebral amyloid plaque load in each case shown in Additional file 10: Figure S3C. We found a strong correlation (r = 0.85) between Aβ levels measured by LFQ and TMT quantitation approaches (Additional file 10: Figure S3B), suggesting that TMT with SPS-MS3 quantification was able to reliably quantify proteins over a large dynamic range, similar to the LFQ approach employed in our previous analysis.

We used the same correlational network analysis approach previously applied to the LFQ data to construct a protein correlational network from the TMT data (Fig. 1a). Whereas we were able to identify 16 modules of coexpressed proteins in the LFQ protein network, the increased proteomic depth afforded by the TMT pipeline increased the number of modules identified in the TMT network to 50. When comparing protein membership overlap between the modules in the two networks, most of the modules that were previously identified in the LFQ network were largely recapitulated in the TMT network, including LFQ modules M1, M4, M6, and others previously identified as strongly correlated with AD pathology [5]. These modules were renumbered in the TMT network due to enlargement of the network, and were occasionally split among several TMT modules, such as TMT modules M7 and M8 that corresponded to the LFQ module M5. TMT modules with cognate modules in the LFQ network that were strongly associated with AD traits included M1 and M3, which were negatively correlated with AD pathology, and M4 and M7, which were positively correlated with AD pathology, among others. However, in addition to the previously identified modules, the increased depth of the TMT network allowed us to identify a number of modules that shared little to no overlap with modules in the LFQ network. For instance, the most “unique” module, module 27 (M27), contained > 70% new protein members that were not identified in the LFQ network (Fig. 1b). The cell type “character” of each module can be assessed by examining the overlap of module protein membership with cell type specific protein expression data [5, 17]. While most of the new modules did not display a strong association with any of the four brain cell types we analyzed (microglia, astrocytes, oligodendrocytes, or neurons), M27 was predominantly glial in nature and correlated positively with tau tangle burden (Braak stage). Other modules unique to the TMT network that were significantly correlated with AD neuropathology included M17 and M29, which were associated with increased tau tangle burden, and M47, which was associated with decreased tau tangle burden. Additional TMT network modules associated with disease are illustrated in Additional file 11: Figure S4 and Supplementary Data. Therefore, the increased depth of proteome coverage afforded by the TMT pipeline allowed us to identify new modules that correlated with disease.

Genetic variants that impart increased or decreased risk for AD have been mapped by genome wide association studies (GWAS), which collectively have identified over 20 genetic loci associated with AD at genome-wide significance, and many other loci that fall below genome-wide significance [12, 25]. We assessed whether the proteins encoded within these AD risk loci preferentially cluster within any of the modules in the TMT network [5, 22]. We found four modules that were uniquely enriched in gene products significantly associated with AD GWAS risk loci: M4, M7, M27, and M33 (Fig. 2). Three out of four of these modules (M4, M7, and M27) were predominantly glial in nature and correlated positively with amyloid plaque and tau tangle burden, whereas one module, M33, contained more proteins associated with oligodendrocytes and neurons, and correlated negatively with amyloid plaques and tau tangles (Fig. 1a). The proteins identified by GWAS that were enriched in each of these modules are highlighted in Additional file 12: Figure S5. Modules M4 and M7 showed strong overlap with two similar modules identified in the LFQ network, M6 and M5, respectively. M5 was also enriched in AD GWAS loci [5]. Module M27, however, was the most unique module in the TMT network compared to the previous LFQ network (Fig. 1b), and showed enrichment in GWAS protein candidates including PICALM, FERMT2, and TMEM106B, among others (Additional file 12: Figure S5). Therefore, the increased depth of the TMT network allowed us to identify unique protein modules that were both glial in nature and strongly enriched for AD genetic risk factors.

The neuropathological changes that are quintessential for AD diagnosis—namely, the development of amyloid plaques and tau tangles—are considered to develop years before onset of the cognitive changes that characterize AD dementia [26]. This asymptomatic phase of AD (AsymAD) is currently considered a preclinical stage of the disease [27, 28]. Because our cohort contained brains from control (little to no AD pathology), AsymAD (AD pathology without cognitive symptoms), and AD (AD pathology with dementia) cases, we were able to examine the changes in brain cell type abundance for four different classes of cells across controls, AsymAD, and AD using a digital sorting algorithm [19, 29], and correlate these changes with amyloid plaque and tau tangle burden (Fig. 3a and Additional file 13: Figure S6). We found that astrocytes and microglia were increased in AD compared to AsymAD and control, and showed a strong correlation with tau tangle burden (Braak stage). The percentage of astrocytes and microglia also correlated with amyloid plaque burden, but less strongly than with tau tangle burden. The neuronal cell population decreased in both AsymAD and AD and correlated negatively with amyloid plaque burden. The oligodendrocyte population increased in AsymAD, and then decreased slightly in AD compared to AsymAD. While there was a weak positive correlation with amyloid plaque burden that approached statistical significance, the fraction of oligodendrocytes did not correlate with tau tangle burden. Therefore, the changes associated with progression to AsymAD are a decrease in neurons and an increase in oligodendrocytes mostly associated with amyloid burden. Progression from AsymAD to AD—that is, symptom onset—is associated with an increase in astrocytes and microglia, and is associated with neurofibrillary tangle burden.

We next asked whether these changes in cell type abundance are the primary drivers of changes in protein abundance among control, AsymAD, and AD, or whether there are changes in protein abundance by disease state that are independent of changes in cell type. TMT proteomic analysis allowed us to identify 1147 proteins that showed changes in abundance among control, AsymAD, and AD cases (Fig. 3b). Most of the proteins with altered abundance were observed when comparing control with AD cases, or AsymAD with AD cases, with relatively fewer proteins that differed between control and AsymAD. To account for changes in cell type on changes in protein abundance between groups, we used our estimates of cell type changes to deconvolute this effect from changes in protein abundance [19, 29], and then reanalyzed our pairwise group comparisons of differentially abundant proteins after deconvolution. This approach has previously been applied to transcriptomic data to remove the confounding effects of cell type changes on gene expression [30], but to our knowledge has not previously been applied to proteomic data. Deconvolution of cell type changes reduced the number of proteins with significantly different abundance levels between disease states (Fig. 3c). The number of proteins with different abundance levels between control and AD was reduced after deconvolution by approximately a factor of six, suggesting that most of the changes in protein abundance observed between control and AD are driven by changes in brain cell type. A similar reduction in abundance changes was observed between control and AsymAD after deconvolution. Notably, however, the number of proteins with unique changes in abundance between AsymAD and AD showed only a small reduction—from 290 to 263 proteins—after deconvolution for cell type, suggesting that most of the changes in protein abundance between AsymAD and AD are not driven primarily by changes in brain cell type. Instead, these changes may reflect a “biochemical phase” of AD [31]. There were slightly more proteins that were significantly lower in abundance compared to those that were higher in abundance in AD compared to AsymAD after cell type deconvolution (Additional file 14: Figure S7). Proteins that were elevated in AD compared to AsymAD included FABP7, SMOC1, and LTF, and tended to cluster in modules M4, M7, and M8. Those that were lower in AD compared to AsymAD included NPTX2, VGF, and GSTM1, and tended to cluster in modules M1, M2, and M3. Most of the modules in which the differentially abundant proteins between AsymAD and AD tended to cluster correlated with case status or AD pathology (Supplementary Data). GO network analysis of differentially abundant proteins between AsymAD and AD showed that many more protein ontologies became significant after cell type deconvolution, and existing ontologies identified in the unregressed analysis such as “cytoskeleton” became more significant (Fig. 3d and e). A GO network analysis of differentially abundant proteins between control and AsymAD cases before and after cell type deconvolution, representing protein changes early in the AD process, is provided in Additional file 15: Figure S8. In summary, these findings suggest that a majority of the differences in protein abundance between AsymAD and AD appear to be independent of simple brain cell type abundance changes, in contrast to the protein abundance differences between control and AsymAD and control and AD. Furthermore, proteins that change in abundance between AD and AsymAD are contained within modules that correlate with AD traits.

After cell type deconvolution of protein abundance changes, we noted with keen interest the preservation of RNA binding proteins as hubs of differentially abundant proteins between control and AsymAD (Additional file 15: Figure S8), and between AsymAD and AD (Fig. 3e and Additional file 16: Figure S9). We have previously reported that aggregation of RNA binding proteins that are a part of the cellular pre-mRNA splicing machinery, especially the U1 small nuclear ribonucleoproteins (snRNPs) such as U1-70K, is an early event in AD pathogenesis [32]. The observation that RNA binding proteins emerged as hubs of differentially abundant proteins after cell type deconvolution prompted us to investigate whether certain TMT network modules were enriched in RNA binding proteins, and if so, whether these modules were associated with AD pathology. Upon examination of a number of different classes of RNA binding proteins, we found that modules 10, 15, 17, 18, 29, and 40 were significantly enriched with RNA binding proteins (Additional file 17: Figure S10A). Most of the RNA binding protein-enriched modules correlated with tau tangle burden as measured by Braak stage (Additional file 17: Figure S10B). Interestingly, our previous studies demonstrated that many of the U1 snRNPs colocalize with neurofibrillary tangles and paired helical filaments in AD brain [24, 32‐34], and that accumulation of insoluble snRNPs correlates strongly with both amyloid and Tau pathology [32‐37]. Collectively, these data support the relevance of this class of proteins to AD pathogenesis. The finding of strong RNA binding protein enrichment in certain modules within the AD TMT network led us to question whether these same modules contained more alternatively spliced proteins whose abundances may change as a consequence of AD pathophysiology. Changes in RNA splicing leading to the expression of different protein isoforms may be a useful indicator of AD pathology and cause downstream cellular and network dysfunction leading to cognitive decline.

Before proceeding with a proteomic analysis of alternative RNA splicing in brain, we compared which mass spectrometry proteomic quantification pipeline—LFQ or TMT—would be most suitable for such an analysis. One aspect of the TMT quantification approach as implemented in our pipeline is that it uses LysC rather than trypsin enzymatic digestion prior to LC-MS/MS. LysC cleaves peptides only at lysine residues, whereas trypsin cleaves at both lysine and arginine residues. LysC therefore generates peptides that are, on average, of greater length than peptides generated after trypsin digestion [13]. We hypothesized that this increased peptide length may better capture exon-exon junction (EEjxn) sites generated through alternative splicing, and we therefore assessed whether the TMT-LysC approach allowed us to identify and quantify more alternatively spliced proteins than our previous LFQ-trypsin approach. A schematic of our approach for splicing analysis is shown in Fig. 4a, with details provided in Methods. We used RNA-seq data from control and AD DLPFC to generate a library of potentially translated polypeptides in silico, which we then digested in silico with LysC to generate proteolytic peptides that could be appended to standard databases for identification and quantification of alternative splicing events by mass spectrometry. An example of an alternative mRNA splicing decision quantified at the peptide level by TMT-MS shown in Fig. 4b. Using this proteogenomic approach, we were able to identify 5746 alternative exon-exon junction (alt-EEjxn) peptides in the LFQ-trypsin analysis, compared to 4830 alt-EEjxn peptides in the TMT-LysC analysis (Additional file 18: Figure S11). However, in the LFQ analysis over 1000 of the alt-EEjxn peptides were identified in only 1 out of 47 samples. When comparing the number of quantifiable alt-EEjxn peptides, the TMT approach was slightly better than LFQ over a range of data missingness thresholds (Additional file 18: Figure S11). However, because our TMT analysis was performed in batch with cases and controls present within the same batch, whereas LFQ is performed on individual cases, the difference in quantifiable alt-EEjxn peptides by case status between TMT and LFQ is larger than simply the difference in total quantifiable alt-EEjxn peptides. This difference is illustrated in Fig. 4c, and demonstrates the advantage of the TMT approach when quantifying proteins across multiple experimental groups. A summary comparison between the TMT-LysC and LFQ-trypsin approaches used here for alt-EEjxn peptide analysis is given in Additional file 5: Table S5, along with the custom databases from which the alt-EEjxn peptides were identified. As shown in Fig. 4c, LFQ-trypsin and TMT-LysC analyses identified and quantified largely separate subsets of alt-EEjxn peptides. Of those alt-EEjxns that were identified and quantified by both LFQ-trypsin and TMT-LysC approaches, the relative quantitative values correlated between the two approaches (Additional file 19: Figure S12), lending validity to the alt-EEjxn quantifications. From both of these analyses we were able to validate the occurrence of a number of alt-EEjxn splicing events in brain that have yet to be annotated in Swiss-Prot, and which heretofore have been predicted to exist only in the Trembl database, in our RNAseq data, or in both. For those alt-EEjxns that were identified in only the RNAseq data, trypsin and LysC also identified different subsets of junctions as reflected by the different GO ontologies for these alternatively spliced proteins, similar to the total alt-EEjxn quantifications (Additional file 20: Figure S13). A complete list and description of each alt-EEjxn peptide identified by LFQ-trypsin and TMT-LysC pipelines is given in Supplementary Data. In summary, we found that our TMT-LysC pipeline was superior to our previous LFQ-trypsin pipeline for quantitative analysis of alt-EEjxn splicing decisions in brain, and we therefore focused our subsequent analyses on alt-EEjxn peptides generated by LysC cleavage and quantified by TMT.

In order to examine which alt-EEjxn splicing events may be associated with progression of cognitive dysfunction from AsymAD to AD given the RNA binding protein abundance differences after cell type deconvolution between these two disease states, we performed a differential abundance analysis of alt-EEjxn peptides between AsymAD and AD. As shown in Fig. 4d, we found there were more alt-EEjxn peptides that were reduced in AD compared to AsymAD, similar to the total protein abundance differences between AD and AsymAD. Alt-EEjxns that were increased in AD were enriched in modules M4, M7, and M35, with M35 containing alt-EEjxns with the largest average change from AsymAD (Additional file 21: Figure S14). All of these modules were strongly glial in nature, with M35 a strongly astrocytic module. Tau had a number of alt-EEjxn peptides that were significantly increased in AD, and these mapped to the 3- and 4- microtubule binding domain repeat isoforms of the protein in this analysis because either isoform can be considered constitutively expressed in humans. Alt-EEjxns that were decreased in AD were most abundant in module M36—a module unique to the TMT network and without strong cell type character. We also analyzed specifically alt-EEjxns derived from the top twenty most significant common variant AD risk factor proteins identified from GWAS [12]. We observed alt-EEjxn peptides from a total of five of these proteins (Additional file 6: Table S6). Three of the five GWAS proteins had alt-EEjxns that were different in abundance by case status, and included BIN1, PTK2B, and FERMT2 (Additional file 7: Table S7). In summary, we identified a number of alternative splicing decisions at the protein level in brain that significantly change in AD, including in AD risk factor proteins identified from GWAS. Those that were increased in AD tended to cluster in astroglial modules.

To extend our analysis of alt-EEjxns to the network level, we next assessed whether certain network modules were enriched for alt-EEjxns beyond those enriched only for differentially abundant alt-EEjxns, and if so, whether these modules were the same modules that were enriched in RNA binding proteins. To do so, we added the alt-EEjxn peptides identified by TMT-LysC to the TMT protein network modules with which they most highly correlated and tested whether a module contained more alt-EEjxn peptides than would be expected by chance. Interestingly, we found that there was little overlap between modules enriched in RNA binding proteins and those enriched in alt-EEjxn peptides (Fig. 4e), suggesting that pre-mRNA splicing changes in AD are not highly correlated with changes in the levels of RNA binding proteins, per se. Modules 4 and 7—glial modules enriched in AD risk factors from GWAS—did not contain more alt-EEjxn peptides than expected, but two other GWAS modules, M27 and M33, were enriched, with M33 being highly enriched. M33 contained snRNPC, a U1 snRNP involved in the spliceosome complex. Another highly enriched module, M15, also contained snRNPs snRNPB and snRNP70 (also known as U1-70K). We have previously shown that U1 snRNPs are a major component of the AD insoluble proteome [24, 32‐34]. Modules that were most highly enriched with alt-EEjxn peptides clustered in the unique (i.e., protein module specific) region of the TMT network, with an especially enriched cluster in related modules 11, 36, 16, and 20. Modules 11 and 20 had corrected p values for enrichment of 4.1e^− 15 and 1.7e^− 13, respectively, and by GO analysis were most likely to be involved in regulation of immune system processes (data not shown). This region of the TMT network was not strongly associated with a particular cell type or strongly correlated with standard histopathological measures of AD. However, given that alternative splicing is a molecular event, we tested whether these modules might better correlate with molecular markers of AD pathology present within the same tissue sample rather than with the general pathological measures represented by CERAD score and Braak stage. For molecular correlation with tau, we correlated each module with the tau pT231 peptide (VAVVRpTPPKSPSSAK), which lies within the proline-rich region of tau and is separate from the microtubule-binding region that aggregates into neurofibrillary tangles. Phosphorylation of tau at T231 has been associated with AD [38], and levels of this peptide are moderately correlated with tau tangle burden (Additional file 10: Figure S3D). We also tested for correlation of each network module with Aβ17–28, U1-70K, and TDP-43, as well as with cognitive function as measured by last MMSE score prior to death. As shown in Fig. 4e, modules 11, 36, and 20 showed a significant correlation with either tau pT231, U1-70K, or cognition. Interestingly, the highly related modules 11 and 36 showed opposite correlation with cognition, with increases in M11 associated with worsened cognitive function and increases in M36 associated with improved cognitive function, suggesting that protein splicing changes may be highly specific in their relationships to different types of AD pathology and cognitive function. Module 35, which contained a number of differentially abundant alt-EEjxns, showed strong correlation to Aβ as well as to tau pT231, and correlated with worsened cognitive function. From the molecular analyses we also identified modules such as M18 that, although not enriched in alt-EEjxn peptides, were observed to be strongly enriched in RNA binding proteins (Additional file 22: Figure S15), were strongly correlated in a positive direction with tau pT231, U1-70K, and TDP-43, and were correlated in a negative direction with cognitive function. A summary of the TMT network findings that includes cell type character, correlation with histological and molecular markers of AD and cognition, and enrichment of RNA binding proteins and protein alt-EEjxns, is given in Fig. 5. Overall, we found that RNA binding proteins are enriched within specific network modules, and that these modules are generally positively correlated with AD pathology and negatively correlated with cognitive function. Modules that are enriched in alt-EEjxns do not overlap significantly with RNA binding protein modules, but many are located within the unique region of the TMT network and correlate with molecular markers of AD pathology. Some of these modules are strongly enriched with alt-EEjxns and correlate with cognitive function.

As a final separate approach to investigate alt-EEjxn splicing events that may be relevant to AD, we correlated each alt-EEjxn peptide with the module eigenprotein for those modules that were enriched in AD GWAS hits in the BLSA-TMT network (M4, M7, M27, and M33), and assessed whether the alt-EEjxn peptide was present in differential abundance among control, AsymAD, and AD brains. A list of the top ten most highly correlated alt-EEjxn peptides with these modules and their differential abundance by case status is given in Table 1. Most of the alt-EEjxn peptides that were highly correlated with the M4 and M7 modules were also present in differential abundance by case status, whereas few of the alt-EEjxn peptides that correlated highly to the M27 and M33 modules had different levels between control, AsymAD, and AD. In module M27, nearly all of the top ten most-highly correlated alt-EEjxn peptides were not present as full proteins within the module, with the exception of FERMT2, which has been identified as an AD risk factor protein. Proteins from which an alt-EEjxn peptide was identified that correlated to glial modules M27 and M33 are annotated as being involved in translation initiation, nucleic acid metabolism, protein folding chaperoning, and cytoskeleton organization, among other cellular functions. Interestingly, the most significant differential abundance in this list of highly correlated alt-EEjxn peptides was between AsymAD and AD, and was for an alt-EEjxn peptide derived from TPI1 (triosephosphate isomerase 1). TPI1 is involved in gluconeogenesis, but has also been identified as interacting directly with the Parkin protein, a ubiquitin protein ligase, and potentially affecting mitochondrial function [39]. Most of the alt-EEjxn peptides that highly correlated to the M27 module were from proteins that are involved in membrane scaffolding, endosomal transport and autophagy, as well as protein translation. We therefore identified a number of alternative splicing events that strongly correlated with network modules enriched in AD GWAS risk factor proteins, and some of these isoforms differed in abundance in AsymAD and AD.