New Interface for Faster Proteoform Analysis: Immunoprecipitation Coupled with SampleStream-Mass Spectrometry.
Henrique Dos Santos SecklerHae-Min ParkCameron M Lloyd-JonesRafael D MelaniJeannie M CamarilloJohn T WilkinsPhilip D ComptonMichael P SnyderPublished in: Journal of the American Society for Mass Spectrometry (2021)
Different proteoform products of the same gene can exhibit differing associations with health and disease, and their patterns of modifications may offer more precise markers of phenotypic differences between individuals. However, currently employed protein-biomarker discovery and quantification tools, such as bottom-up proteomics and ELISAs, are mostly proteoform-unaware. Moreover, the current throughput for proteoform-level analyses by liquid chromatography mass spectrometry (LCMS) for quantitative top-down proteomics is incompatible with population-level biomarker surveys requiring robust, faster proteoform analysis. To this end, we developed immunoprecipitation coupled to SampleStream mass spectrometry (IP-SampleStream-MS) as a high-throughput, automated technique for the targeted quantification of proteoforms. We applied IP-SampleStream-MS to serum samples of 25 individuals to assess the proteoform abundances of apolipoproteins A-I (ApoA-I) and C-III (ApoC-III). The results for ApoA-I were compared to those of LCMS for these individuals, with IP-SampleStream-MS showing a >7-fold higher throughput with >50% better analytical variation. Proteoform abundances measured by IP-SampleStream-MS correlated strongly to LCMS-based values (R2 = 0.6-0.9) and produced convergent proteoform-to-phenotype associations, namely, the abundance of canonical ApoA-I was associated with lower HDL-C (R = 0.5) and glycated ApoA-I with higher fasting glucose (R = 0.6). We also observed proteoform-to-phenotype associations for ApoC-III, 22 glycoproteoforms of which were characterized in this study. The abundance of ApoC-III modified by a single N-acetyl hexosamine (HexNAc) was associated with indices of obesity, such as BMI, weight, and waist circumference (R ∼ 0.7). These data show IP-SampleStream-MS to be a robust, scalable workflow for high-throughput associations of proteoforms to phenotypes.
Keyphrases
- mass spectrometry
- liquid chromatography
- high throughput
- body mass index
- high resolution mass spectrometry
- gas chromatography
- high performance liquid chromatography
- capillary electrophoresis
- high resolution
- tandem mass spectrometry
- weight gain
- multiple sclerosis
- public health
- healthcare
- type diabetes
- gene expression
- insulin resistance
- physical activity
- blood glucose
- metabolic syndrome
- weight loss
- small molecule
- ms ms
- solid phase extraction
- machine learning
- single cell
- genome wide
- deep learning
- cross sectional
- risk assessment
- copy number
- artificial intelligence
- dna methylation
- skeletal muscle
- mental health
- climate change
- high fat diet induced
- genome wide identification
- data analysis