Enhancing Top-Down Proteomics Of Brain Tissue With FAIMS Part 3

One immediate and apparent observation is that the higher the abundance percentile, the higher the probability of identification with TDP. In other words, TDP mostly identifies highly abundant proteins. 

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About half of the genes identified from the top-down data sets (with or without FAIMS) were contained within the top 20% of abundance bins for the bottom-up analysis (Figure 6A). Conversely, 71 genes could only be found in the top-down data sets ("NA" bin) (Figure 6A). 

These genes appeared to represent relatively short proteins (<150 AA) and contained numerous basic Lys/Arg residues which, presumably, precluded their ability to be detected by bottom-up analysis. 

For example, included within the top-down only bin were histone proteins such as H3C1, H4C1, H1–4, and H2BC12. As anticipated, we found that FAIMS could improve identifications in lower-abundant percentiles over "No FAIMS", increasing the depth of the observable proteome. 

By determining the ratio of FAIMS to "No FAIMS" across all percentiles, an appreciable increase in identifications below the 40th percentile is apparent within CVs in the −40 to −50 range (Figure 6B). 

However, when considering the absolute number of genes, the major contribution of FAIMS' advantage is through broadening the spectrum of identifications across all abundance percentiles.

Relationship between FAIMS CV and Transmission of Proteoforms

With FAIMS, we also noted a trend between FAIMS CV and proteoform molecular weight (Figure 7). At −50 CV, the median proteoform mass is ∼5 kDa and increases to ∼15 kDa at −20 CV. 

Based on these mass distributions (Figure 7), CVs less than −50 V appear well suited for top-down or middle-down proteomic experiments, while CVs greater than −50 V may best benefit peptidomic or bottom-up experiments. 

The observed molecular-weight trend presumably extends beyond −20 CV; however, we restricted our search to −20 CV based on the drop-off in proteoform and gene identifications which led to diminishing returns in proteome sequence coverage per run. 

We should note that a previous work using FAIMS found +40 CV to be the ideal voltage for transmitting a NIST mAb heavy chain (∼51 kDa) and −20 CV was best for the corresponding light chain (∼23 kDa), supporting the idea that the trend we observe extends into positive FAIMS CVs.45 

Interestingly, although many spectra identifying a single proteoform were limited to being found within a 10 V range (3599 out of 5165), there were seven proteoforms observed across the entire CV range from −50 to −20. Presumably, this can be attributed to their high abundance in the sample, particularly in the case of ubiquitin (UBB), myelin basic protein, and acylCoA-binding protein. 

However, it is also likely that the different charge states of these proteoforms adopt several gas-phase conformations depending on their proton isomerization, impacting their mobilities.42 

These seven proteoforms allowed us to investigate how the charge state envelope is differentially transmitted through the modulation of CV. As a proxy for the charge state distribution at each CV, we used the median charge state based on the proteoform-spectrum-matches (PrSMs) identified at each CV and tracked how this value changed as a function of CV starting at −50 CV. 

The MS1 scans in Figure S3 demonstrate how the median PrSM charge state tracks the charge state distribution of UBB. Of these seven proteoforms, four followed an inverse relationship, with the median observed charge state increasing as CV was decreased (Figure 8). 

It is worth noting that this trend has generally been observed with peptides and small proteins.56,62–64 Surprisingly, three proteoforms showed the opposite relationship where decreasing CV decreased the median charge states observed (Figure 8). 

A cursory examination of the mean precursor mass between the two groups suggests that larger precursors are more likely to favor higher charge states as CV is decreased. 

To validate these relationships further, we expanded this analysis to include proteoforms that were identified within a more modest, but still wide, 20–30 CV range (n = 256 proteoforms). Here, proteoforms whose median charge shifted greater than one charge across the entire CV range were binned into two different groups depending on the direction of that shift. 

Those that shifted less than one charge were considered "neutral" concerning changing CV. Similar to the previous results, larger precursors were significantly more likely to be associated with an inverse relationship between observed charge states and CV (Table S2). 

With decreasing CV, the majority of proteoforms appeared to transmit at lower charge states (n = 177) compared to those that favored higher charge states (n = 39). The remainder (n = 40) were considered "neutral" concerning changes in CV. 

Proteoforms binned within the neutral group demonstrated an average precursor mass between the inverse and direct groups, once again suggesting that the mass of the proteoform is intrinsically linked to this behavior. 

Other primary sequence-based parameters such as basic/acidic amino acid composition and aliphatic index were not significantly correlated, however (Table S2). 

Although the proteins being introduced into the gas phase are presumably denatured, factors typically associated with native proteins such as dipole moment or collisional-cross section may have better predictive value toward this phenomenon.64–66 Our results demonstrate how CV can be used to filter proteins of different masses, and how a protein's charge state envelope can be differentially transmitted through FAIMS as well.

Utility of Protein Fragments in TDP Experiments

Surprisingly, a significant number of proteoforms we identified were fragments of larger proteins. We found that only 25% of the unique proteoforms identified covered greater than half of the protein's sequence from which they were derived. 

This 25% had an average mass of 11.4 kDa compared to the remaining 75% which had an average mass of 5.7 kDa. Several factors may contribute to this observation, some of which are independent of FAIMS. 

For example, these fragments themselves may be proteolytic cleavage products produced under normal homeostatic conditions as part of the cellular "degradome",67 or despite the various precautions taken, introduced during the post-mortem interval and sample handling. 

Central nervous system tissue is a rich source of signaling peptides known as "neuropeptides" that are commonly derived from much larger precursor proteins as well.68 

By cross-referencing our proteoform identifications with an established neuropeptide database (NeuroPedia),69 we were able to identify several such neuropeptides including vasostatin-1, secretoneurin, cholecystokinin-58 desnonopeptide, and neuropeptide y and many non-canonical sequence variants derived from the known neuropeptide-producing genes. 

Beyond biological factors, certain instrument parameters can influence the observation of protein fragments as well. For example, low in-source "fragmentation" voltages (between 10 and 20 V) can typically be used to remove adducts and desolvate protein ions, while higher voltages can produce source-induced dissociation. 

However, the susceptibility of amide bonds to dissociation can vary widely, and as the size of a protein increases so does the likelihood of it containing labile amide bonds such as Xaa-Pro.70–73 As b- and y-ions may be produced inadvertently through this mechanism, even at low source voltages, we decided to eschew applying source voltage to reduce the chance of introducing fragment ions into the instrument. 

It is also possible that proteins are susceptible to fragmentation events as they pass through the electric fields created in FAIMS. However, these fragments would not be expected to have the same mobility as the parent ion, with the caveat that this is likely dependent on the size of the fragment relative to the precursor ion as well. 

Finally, it is worth pointing out a few factors that can bias toward the observation of smaller proteoforms (<∼15 kDa). First and foremost is the signal spreading that occurs as the size of a proteoform increases.74 

This signal spreading can largely be attributed to the charge state envelope and isotopic distributions of each charge state. Specific to our experimental setup was the use of a size 3K MWCO filter as the final filtration step in our sample prep, which in this case was chosen to ensure that smaller amyloid beta proteoforms could be efficiently captured if they were present. 

General instrumental factors can create a bias toward small proteoforms as well, including the tuning of the quadrupole, electrodynamic capture, and collisions with residual gas molecules in the Orbitrap cell that lead to quicker decay of the transient for larger molecules.75 Irrespective of these biases or the exact origin of the fragments, they can still provide insights into the proteome. 

These fragments are particularly useful in the context of neurodegenerative disease where disruptions in proteostasis are commonly linked to pathology.76,77 

Indeed, fragments of tau are often found to be neurotoxic and play a role in the progression of tauopathies such as AD.23–26 Generally, the fragments we observe are much larger than tryptic peptides, and in several cases where there are many fragments for a particular protein, we observe considerable sequence coverage. 

This is exemplified by examining the fragments providing coverage for the ∼50 kDa tubulin alpha-1B chain (TUBA1B) and the ∼70 kDa synapsin-1 (SYN1), as shown in Figure 9A, B, both large proteins that would otherwise be difficult to observe in their full-length form in TDP of a complex sample.

Identification of Proteoforms with Relevance to Neurodegenerative Disease

The utilization of FAIMS with our top-down analysis enabled the identification of several unique Swiss-Prot splice variants and TrEMBL entries, with 267 unique splice variants and 96 TrEMBL entries (Table S3). 

Notably, we observe multiple PrSMs unambiguously identifying an alternative ORF isoform (A0A0D9SF30) for neural cell adhesion molecule 1 (NCAM1). We also observed proteoforms derived from genes that have known roles in several neurodegenerative diseases, in particular α-synuclein and PARK7. 

In both of these cases, the predominant proteoforms contain the full-length sequence. Interestingly, the majority of α-synuclein, βsynuclein, and (to a lesser extent) γ-synuclein PrSMs were found to contain a ∼177 Da unknown mass shift near the C-terminus (full-length α-synuclein spectrum with unknown modification, as shown in Figure S4A). 

This mass shift has been previously observed in open database searches and is generally found at Asp or Glu residues.78 A potential explanation that closely matches the average and monoisotopic mass of the modification consists of one oxygen with three iron atoms as well as the loss of seven hydrogen atoms (based on Unimod accession #1971). 

Comparison of the isotopic peaks of a y242+ fragment ion from α-synuclein (Figure S4B) with a simulated spectrum containing the aforementioned elements that are believed to belong to the unknown modification (Figure S4C) demonstrates the similarity between the two isotopic distributions, with many of the isotopic peaks aligning within 2 ppm of each other. 

It should also be pointed out that the leftmost isotopic peaks are unique to naturally occurring iron isotopes, and the absence of these peaks is readily apparent when the spectrum is simulated without containing the three iron atoms (Figure S4D), strongly suggesting that this unknown modification is very likely composed of the proposed elements. 

Prior studies have also described the high affinity of α-synuclein for various metal ions, and the region we observe to contain this modification overlaps with residues known to be involved in binding―specifically the 119DPDNEA124 motif (Figure S5).79–81 

Our data also suggest that the C-terminal regions of β-synuclein and γ-synuclein have similar roles in metal binding as multiple spectra with the same unknown mass shift were matched to similar sequences within these proteins.

About the mitochondrial protein PARK7, we noted a ∼116.0 Da mass shift on its single active site Cys residue, C106 (Figure S6). One potential explanation with a similar delta mass is succinylation, a modification that is attributed to mitochondrial stress and forms due to Michael's addition of fumarate onto a Cys thiol group.82

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