MIND: A Double-Linear Model To Accurately Determine Monoisotopic Precursor Mass in High-Resolution Top-Down Proteomics

Top-down proteomics approaches are becoming ever more popular, due to the advantages offered by knowledge of the intact protein mass in correctly identifying the various proteoforms that potentially arise due to point mutation, alternative splicing, post-translational modifications, etc. Usually, the average mass is used in this context; however, it is known that this can fluctuate significantly due to both natural and technical causes. Ideally, one would prefer to use the monoisotopic precursor mass, but this falls below the detection limit for all but the smallest proteins. Methods that predict the monoisotopic mass based on the average mass are potentially affected by imprecisions associated with the average mass. To address this issue, we have developed a framework based on simple, linear models that allows prediction of the monoisotopic mass based on the exact mass of the most-abundant (aggregated) isotope peak, which is a robust measure of mass, insensitive to the aforementioned natural and technical causes. This linear model was tested experimentally, as well as in silico, and typically predicts monoisotopic masses with an accuracy of only a few parts per million. A confidence measure is associated with the predicted monoisotopic mass to handle the off-by-one-Da prediction error. Furthermore, we introduce a correction function to extract the “true” (i.e., theoretically) most-abundant isotope peak from a spectrum, even if the observed isotope distribution is distorted by noise or poor ion statistics. The method is available online as an R shiny app: https://valkenborg-lab.shinyapps.io/mind

On the fine isotopic distribution and limits to resolution in mass spectrometry

Mass spectrometry enables the study of increasingly larger biomolecules with increasingly higher resolution, which is able to distinguish between fine isotopic variants having the same additional nucleon count, but slightly different masses. Therefore, the analysis of the fine isotopic distribution becomes an interesting research topic with important practical applications. In this paper, we propose the comprehensive methodology for studying the basic characteristics of the fine isotopic distribution. Our approach uses a broad spectrum of methods ranging from generating functions—that allow us to estimate the variance and the information theory entropy of the distribution—to the theory of thermal energy fluctuations. Having characterized the variance, spread, shape, and size of the fine isotopic distribution, we are able to indicate limitations to high resolution mass spectrometry. Moreover, the analysis of “thermorelativistic” effects (i.e., mass uncertainty attributable to relativistic effects coupled with the statistical mechanical uncertainty of the energy of an isolated ion), in turn, gives us an estimate of impassable limits of isotopic resolution (understood as the ability to distinguish fine structure peaks), which can be moved further only by cooling the ions. The presented approach highlights the potential of theoretical analysis of the fine isotopic distribution, which allows modeling the data more accurately, aiming to support the successful experimental measurements. [Figure: see text] ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1007/s13361-015-1180-4) contains supplementary material, which is available to authorized users

Fractionated irradiation of MCF7 breast cancer cells rewires a gene regulatory circuit towards a treatment\u2010resistant stemness phenotype

Radiotherapy is the standard of care for breast cancer. However, surviving radioresistant cells can repopulate following treatment and provoke relapse. Better understanding of the molecular mechanisms of radiation resistance may help to improve treatment of radioresistant tumours. To emulate radiation therapy at the cellular level, we exposed MCF7 breast cancer cells to daily radiation doses of 2 Gy up to an accumulated dose of 20 Gy. Fractionally irradiated cells (FIR20) displayed increased clonogenic survival and population doubling time as compared with age‐matched sham‐irradiated cells and untreated parental MCF7 cells. RNA‐sequencing revealed a core signature of 229 mRNAs and 7 circular RNAs of which the expression was significantly altered in FIR20 cells. Dysregulation of several top genes was mirrored at the protein level. The FIR20 cell transcriptome overlapped significantly with canonical radiation response signatures and demonstrated a remarkable commonality with radiation and endocrine therapy resistance expression profiles, suggesting crosstalk between both acquired resistance pathways, as indicated by reduced sensitivity to tamoxifen cytotoxicity of FIR20 cells. Using predictive analyses and functional enrichment, we identified a gene‐regulatory network that promotes stemness and inflammatory signalling in FIR20 cells. We propose that these phenotypic traits render breast cancer cells more radioresistant but may at the same time serve as potential targets for combination therapies