Frank Koopmans

Identifying true protein complex constituents in interaction proteomics: The example of the DMXL2 protein complex

A typical high‐sensitivity antibody affinity purification‐mass spectrometry experiment easily identifies hundreds of protein interactors. However, most of these are non‐valid resulting from multiple causes other than interaction with the bait protein. To discriminate true interactors from off‐target recognition, we propose to differentially include an (peptide) antigen during the antibody incubation in the immuno‐precipitation experiment. This contrasts the specific antibody–bait protein interactions, versus all other off‐target protein interactions. To exemplify the power of the approach, we studied the DMXL2 interactome. From the initial six immuno‐precipitations, we identified about 600 proteins. When filtering for interactors present in all anti‐DMXL2 antibody immuno‐precipitation experiments, absent in the bead controls, and competed off by the peptide antigen, this hit list is reduced to ten proteins, including known and novel interactors of DMXL2. Together, our approach enables the use of a wide range of available antibodies in large‐scale protein interaction proteomics, while gaining specificity of the interactions.

Interaction Proteomics Reveals Brain Region-Specific AMPA Receptor Complexes

Fast excitatory synaptic transmission in the brain is mediated by glutamate acting on postsynaptic AMPA receptors. Recent studies have revealed a substantial number of AMPA receptor auxiliary proteins, which potentially contribute to the regulation of AMPA receptor trafficking, subcellular receptor localization, and receptor gating properties. Here we examined the AMPA receptor interactomes from cortex, hippocampus, and cerebellum by comprehensive interaction proteomics. The study reveals that AMPA receptor auxiliary proteins are engaged in distinct brain region-specific AMPA receptors subcomplexes, which might underlie brain region-specific differential regulation of AMPA receptor properties. Depending on the brain region, an interacting protein can be involved in an AMPA and a non-AMPA receptor complex.

Interaction proteomics of canonical Caspr2 (CNTNAP2) reveals the presence of two Caspr2 isoforms with overlapping interactomes

Autism is a human developmental brain disorder characterized by impaired social interaction and communication. Contactin-associated protein-like 2 (Caspr2, CNTNAP2) is a known genetic risk factor of autism. However, how this protein might contribute to pathology is unclear. In this study, we demonstrate that Caspr2 is abundantly present in lipid raft and in the synaptic membrane but is highly depleted in the postsynaptic density. The Caspr2 protein level in hippocampus is present at a constant level during synapse formation and myelination from P0 to P84. Interaction proteomics revealed the interactors of Caspr2, including CNTN2, KCNAs, members of the ADAM family (ADAM22, ADAM23 and ADAM11), members of LGI family and MAGUKs (DLGs and MPPs). Interestingly, a short form of Caspr2 was detected, which lacks most of the extracellular domains, however, is still associated with ADAM22 and to a lesser extent LGI1 and Kv1 channels. The comprehensive Caspr2 interactome revealed here might aid in understanding the molecular mechanisms underlying autism. This article is part of a Special Issue titled Neuroproteomics: Applications in Neuroscience and Neurology.

Empirical Bayesian random censoring threshold model improves detection of differentially abundant proteins.

A challenge in proteomics is that many observations are missing with the probability of missingness increasing as abundance decreases. Adjusting for this informative missingness is required to assess accurately which proteins are differentially abundant. We propose an empirical Bayesian random censoring threshold (EBRCT) model that takes the pattern of missingness in account in the identification of differential abundance. We compare our model with four alternatives, one that considers the missing values as missing completely at random (MCAR model), one with a fixed censoring threshold for each protein species (fixed censoring model) and two imputation models, k-nearest neighbors (IKNN) and singular value thresholding (SVTI). We demonstrate that the EBRCT model bests all alternative models when applied to the CPTAC study 6 benchmark data set. The model is applicable to any label-free peptide or protein quantification pipeline and is provided as an R script.

Correlation profiling of brain sub-cellular proteomes reveals co-assembly of synaptic proteins and subcellular distribution

Protein correlation profiling might assist in defining co-assembled proteins and subcellular distribution. Here, we quantified the proteomes of five biochemically isolated mouse brain cellular sub-fractions, with emphasis on synaptic compartments, from three brain regions, hippocampus, cortex and cerebellum. We demonstrated the expected co-fractionation of canonical synaptic proteins belonging to the same functional groups. The enrichment profiles also suggested the presence of many novel pre- and post-synaptic proteins. Using super-resolution microscopy on primary neuronal culture we confirmed the postsynaptic localization of PLEKHA5 and ADGRA1. We further detected profound brain region specific differences in the extent of enrichment for some functionally associated proteins. This is exemplified by different AMPA receptor subunits and substantial differences in sub-fraction distribution of their potential interactors, which implicated the differences of AMPA receptor complex compositions. This resource aids the identification of proteins partners and subcellular distribution of synaptic proteins.

Tomosyn associates with secretory vesicles in neurons through its N- and C-terminal domains

The secretory pathway in neurons requires efficient targeting of cargos and regulatory proteins to their release sites. Tomosyn contributes to synapse function by regulating synaptic vesicle (SV) and dense-core vesicle (DCV) secretion. While there is large support for the presynaptic accumulation of tomosyn in fixed preparations, alternative subcellular locations have been suggested. Here we studied the dynamic distribution of tomosyn-1 (Stxbp5) and tomosyn-2 (Stxbp5l) in mouse hippocampal neurons and observed a mixed diffuse and punctate localization pattern of both isoforms. Tomosyn-1 accumulations were present in axons and dendrites. As expected, tomosyn-1 was expressed in about 75% of the presynaptic terminals. Interestingly, also bidirectional moving tomosyn-1 and -2 puncta were observed. Despite the lack of a membrane anchor these puncta co-migrated with synapsin and neuropeptide Y, markers for respectively SVs and DCVs. Genetic blockade of two known tomosyn interactions with synaptotagmin-1 and its cognate SNAREs did not abolish its vesicular co-migration, suggesting an interplay of protein interactions mediated by the WD40 and SNARE domains. We hypothesize that the vesicle-binding properties of tomosyns may control the delivery, pan-synaptic sharing and secretion of neuronal signaling molecules, exceeding its canonical role at the plasma membrane.

Comparative Analyses of Data Independent Acquisition Mass Spectrometric Approaches: DIA, WiSIM-DIA and Untargeted DIA

Data‐independent acquisition (DIA) is an emerging technology for quantitative proteomics. Current DIA focusses on the identification and quantitation of fragment ions that are generated from multiple peptides contained in the same selection window of several to tens of m/z. An alternative approach is WiSIM‐DIA, which combines conventional DIA with wide‐SIM (wide selected‐ion monitoring) windows to partition the precursor m/z space to produce high‐quality precursor ion chromatograms. However, WiSIM‐DIA has been underexplored; it remains unclear if it is a viable alternative to DIA. We demonstrate that WiSIM‐DIA quantified more than 24 000 unique peptides over five orders of magnitude in a single 2 h analysis of a neuronal synapse‐enriched fraction, compared to 31 000 in DIA. There is a strong correlation between abundance values of peptides quantified in both the DIA and WiSIM‐DIA datasets. Interestingly, the S/N ratio of these peptides is not correlated. We further show that peptide identification directly from DIA spectra identified >2000 proteins, which included unique peptides not found in spectral libraries generated by DDA.

Single-cell isoform RNA sequencing (ScISOr-Seq) across thousands of cells reveals isoforms of cerebellar cell types.

Full-length isoform sequencing has advanced our knowledge of isoform biology1–11. However, apart from applying full-length isoform sequencing to very few single cells12,13, isoform sequencing has been limited to bulk tissue, cell lines, or sorted cells. Single splicing events have been described for <=200 single cells with great statistical success14,15, but these methods do not describe full-length mRNAs. Single cell short-read 3’ sequencing has allowed identification of many cell sub-types16–23, but full-length isoforms for these cell types have not been profiled. Using our new method of single-cell-isoform-RNA-sequencing (ScISOr-Seq) we determine isoform-expression in thousands of individual cells from a heterogeneous bulk tissue (cerebellum), without specific antibody-fluorescence activated cell sorting. We elucidate isoform usage in high-level cell types such as neurons, astrocytes and microglia and finer sub-types, such as Purkinje cells and Granule cells, including the combination patterns of distant splice sites6–9,24,25, which for individual molecules requires long reads. We produce an enhanced genome annotation revealing cell-type specific expression of known and 16,872 novel (with respect to mouse Gencode version 10) isoforms (see isoformatlas.com). ScISOr-Seq describes isoforms from >1,000 single cells from bulk tissue without cell sorting by leveraging two technologies in three steps: In step one, we employ microfluidics to produce amplified full-length cDNAs barcoded for their cell of origin. This cDNA is split into two pools: one pool for 3’ sequencing to measure gene expression (step 2) and another pool for long-read sequencing and isoform expression (step 3). In step two, short-read 3’-sequencing provides molecular counts for each gene and cell, which allows clustering cells and assigning a cell type using cell-type specific markers. In step three, an aliquot of the same cDNAs (each barcoded for the individual cell of origin) is sequenced using Pacific Biosciences (“PacBio”)1,2,4,5,26 or Oxford Nanopore3. Since these long reads carry the single-cell barcodes identified in step two, one can determine the individual cell from which each long read originates. Since most single cells are assigned to a named cluster, we can also assign the cell’s cluster name (e.g. “Purkinje cell” or “astrocyte”) to the long read in question (Fig 1A) – without losing the cell of origin of each long read.

Single-cell isoform RNA sequencing characterizes isoforms in thousands of cerebellar cells

Full-length RNA sequencing (RNA-Seq) has been applied to bulk tissue, cell lines and sorted cells to characterize transcriptomes, but applying this technology to single cells has proven to be difficult, with less than ten single-cell transcriptomes having been analyzed thus far. Although single splicing events have been described for ≤200 single cells with statistical confidence, full-length mRNA analyses for hundreds of cells have not been reported. Single-cell short-read 3′ sequencing enables the identification of cellular subtypes, but full-length mRNA isoforms for these cell types cannot be profiled. We developed a method that starts with bulk tissue and identifies single-cell types and their full-length RNA isoforms without fluorescence-activated cell sorting. Using single-cell isoform RNA-Seq (ScISOr-Seq), we identified RNA isoforms in neurons, astrocytes, microglia, and cell subtypes such as Purkinje and Granule cells, and cell-type-specific combination patterns of distant splice sites. We used ScISOr-Seq to improve genome annotation in mouse Gencode version 10 by determining the cell-type-specific expression of 18,173 known and 16,872 novel isoforms.

MIR137 schizophrenia-associated locus controls synaptic function by regulating synaptogenesis, synapse maturation and synaptic transmission

Abstract The MIR137 locus is a replicated genetic risk factor for schizophrenia. The risk-associated allele is reported to increase miR-137 expression and miR-137 overexpression alters synaptic transmission in mouse hippocampus. We investigated the cellular mechanisms underlying these observed effects in mouse hippocampal neurons in culture. First, we correlated the risk allele to expression of the genes in the MIR137 locus in human postmortem brain. Some evidence for increased MIR137HG expression was observed, especially in hippocampus of the disease-associated genotype. Second, in mouse hippocampal neurons, we confirmed previously observed changes in synaptic transmission upon miR-137 overexpression. Evoked synaptic transmission and spontaneous release were 50% reduced. We identified defects in release probability as the underlying cause. In contrast to previous observations, no evidence was obtained for selective synaptic vesicle docking defects. Instead, ultrastructural morphometry revealed multiple effects of miR-137 overexpression on docking, active zone length and total vesicle number. Moreover, proteomic analyses of neuronal protein showed that expression of Syt1 and Cplx1, previously reported as downregulated upon miR-137 overexpression, was unaltered. Immunocytochemistry of synapses overexpressing miR-137 showed normal Synaptotagmin1 and Complexin1 protein levels. Instead, our proteomic analyses revealed altered expression of genes involved in synaptogenesis. Concomitantly, synaptogenesis assays revealed 31% reduction in synapse formation. Taken together, these data show that miR-137 regulates synaptic function by regulating synaptogenesis, synaptic ultrastructure and synapse function. These effects are plausible contributors to the increased schizophrenia risk associated with miR-137 overexpression.