Optimized MS quantitation of knockdown, transcription factor profiles & isoform modulation during stem cell differentiation

Label‐free mass spectrometry (MS) is rapidly emerging as an alternative to antibody‐based methods and there is a continuing need to maximize the accuracy of measurement. Here we describe Spectral Ion Fingerprint Recognition (SpIFR) for optimized quantitation within proteomic datasets derived from liquid chromatography tandem MS. Sets of optimal peptides show that intermediate, rather than high, MS signal is often best for quantitation and produce a high level of accuracy while also providing a global assessment of protein change. In application, we demonstrate a unique elucidation of ratios between isoforms, species‐specific peptide tracking, and selection of ‘housekeeping’ peptide sets for normalization. We focus initially on siRNA knockdown of nuclear lamina proteins that are tightly regulated in stem cells, cancer, and aging, and illustrate the approach further with other targets. By using the knockdown as reference, we show large differences in lamin isoform abundance between different types of stem cells, highlighting a major remodeling of nuclear architecture during differentiation. We extend our method to study other knockdowns, in particular the low‐abundance transcription factors that direct cell lineage decisions.