NeuroLINCS Proteomics: Defining human-derived iPSC proteomes and protein signatures of pluripotency

Abstract

The National Institute of Health (NIH) Library of integrated network-based cellular signatures (LINCS) program is premised on the generation of a publicly available data resource of cell-based biochemical responses or "signatures" to genetic or environmental perturbations. NeuroLINCS uses human inducible pluripotent stem cells (hiPSCs), derived from patients and healthy controls, and differentiated into motor neuron cell cultures. This multi-laboratory effort strives to establish i) robust multi-omic workflows for hiPSC and differentiated neuronal cultures, ii) public annotated data sets and iii) relevant and targetable biological pathways of spinal muscular atrophy (SMA) and amyotrophic lateral sclerosis (ALS). Here, we focus on the proteomics and the quality of the developed workflow of hiPSC lines from 6 individuals, though epigenomics and transcriptomics data are also publicly available. Known and commonly used markers representing 73 proteins were reproducibly quantified with consistent expression levels across all hiPSC lines. Data quality assessments, data levels and metadata of all 6 genetically diverse human iPSCs analysed by DIA-MS are parsable and available as a high-quality resource to the public.

Fig. 1

NeuroLINCS project overview. (a) Omic data generation centers include transcriptomics, proteomics, epigenomics, robotic imaging assays and cell-based functional studies. Molecular signatures of disease from epigenetic, RNA and protein analyses are incorporated into integrative network analysis using Omic Integrator. (b) Samples generated from 12 patient-derived hiPSC lines. Proteomics data is generated using DIA-MS methods on TripleTOFs (Sciex) and searched using sample specific peptide spectral libraries generated from DIA-MS of pooled samples. Analyte signals are extracted using OpenSWATH and MapDIA as specified.

Fig. 2

Data consistency across sample analyses. (a) Number of proteins, peptides and transitions identified per sample, including biological growth replicates and temporally dispersed technical replicates. MS raw file and sample metadata are available in the online Supplementary Table 4. (b) nomenclature key for data labeling, cell line, growth replicate, biological condition, and biological growth replicate. (c) distribution of raw un-normalized protein quantitation log₂ signal intensity. (d) normalized protein concentrations were calculated by dividing each transition intensity by the sum of transitions measured for that sample. Outlier iRT protein data points (circled) in samples with higher iRT to total protein ratios. (e) log₂ intensity of iRT per sample after normalization. iRT measurements greater than 25 are highlighted red. (f) statistics of proteins identified across all data files (n = 38). (g) distribution of protein coefficient of variation (CV) for biological replicates of each cell line. CV for all biological replicates, per bar, and technical replicates. (h) CVs calculated in pairs for each biological replicate. Calculated CVs provides resolution of data quality assessments.

Fig. 3

Reproducibly quantified protein markers of human iPSC lines. (a) 73 protein markers of pluripotency reproducibly quantified in 6 patient-derived iPSC lines. Protein quantitation was averaged across all growth and technical replicates for each cell line. (b) Heatmap of 73 protein hiPSC markers. (c) Extracted ion chromatograms (XICs) for peptide K.LYPAIPAAR.R [562, 570] of DNA (cytosine-5)-methyltransferase 3B in hiPSC lines 14i and 25i for three biological growth replicates. (d) Skyline peak area plot of K.LYPAIPAAR.R [562, 570] transition ions. (e) unbiased sample clustering of DIA-MS hiPSC samples and replicates by hierarchical clustering and (f) unbiased sample clustering by PCA.