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High resolution mobility-based ion separations in Structures for Lossless Ion Manipulations (SLIM) have been useful for ion mobility separations for a variety of molecular classes in the gas phase. Here, we present multi-pass SLIM separations for gas-phase proteins in their near-native state exhibiting charge state dependent arrival time distributions using carbonic anhydrase (29 kDa), alcohol dehydrogenase (148 kDa), and apo-transferrin (79 kDa). For the selected charge states of each protein species, we investigate the conformational space using molecular dynamic simulations and calculated the collision cross section (CCS) values using IMoS. The measured CCS values obtained from the SLIM arrival time distributions (ATDs) agreed within ~6% difference when compared to the calculated CCS values. The experimental CCS values were obtained from calibration curves for the arrival times of Agilent Tune Mix ions. For multi-pass separations, the ATDs were converted to CCS values by deconvoluting the multi-pass arrival times into accurate single-pass values amenable to the single-pass calibration curves. Mass spectra of carbonic anhydrase (CA) showed three different charge states (z = 9+ to 11+). Their corresponding mobility peaks were baseline-separated using 8-m single-pass separations. Single-pass analysis of alcohol dehydrogenase (ADH) exhibit three predominant charge states (z = 23+ to 25+) with mobility overlap between adjacent charge states. The mobility peak resolution for ADH improved with multi-pass separations (up to 24-m path length). In addition, CCS distributions obtained for charge states z = 16+ to 18+ of apo-transferrin reveal a transition from a compact unimodal form (z = 18+ and 19+) to broader multi-modal CCS distributions for z = 16+. For apo-transferrin, 40-m multi-pass separations were performed allowing for complete isolation of the selected mobility range corresponding to z = 17+ leading to selective isolation of a narrow arrival time window. The extended mobility separations provided minimal alterations to the structure of the proteins, and the experimentally derived CCS values showed minimal change as a function of separation time or number of passes. Mobility-based ion separations for native-like proteins, using SLIM, open opportunities for native-IMS applications as well as other manipulations enabled by SLIM like mobility selective isolation and collection. 

Synopsis Basic science research, also called “curiosity-driven research,” is fundamental work done with no immediate economic goals but rather a focus on discovery for discovery’s sake. However, basic science research is often needed to seed more applied, economically oriented, research. Both basic and applied research efforts are important aspects of the “bioeconomy,” defined here as the contributions to the overall economy from various biology-related fields spanning everything from museum-based natural history research to agricultural food and material production to healthcare. Here, we propose that more collaborative efforts across federal granting agencies in a venture-capitalist-like “PO-driven model” can help drive applied innovation from collaborations facilitated by program officers (POs). POs from NSF, DOE, DARPA, USDA, NASA, and other federal agencies should seek out parallel and complementary research ideas from grantees and provide funds to build teams of researchers who may otherwise be unaware of one another. Researchers working in different fields may also be unaware that the different organisms they are studying independently may have evolved similar traits (i.e., convergent evolution) that POs may recognize and who can then facilitate novel research avenues connecting those independent researchers (we provide examples of some projects inspired by convergent evolution here). In this top-down approach to research funding, the US bioeconomy will be pouring fuel on the fire of scientific productivity in this country. 

Abstract Charge ordering (CO), characterized by a periodic modulation of electron density and lattice distortion, has been a fundamental topic in condensed matter physics, serving as a potential platform for inducing novel functional properties. The charge-ordered phase is known to occur in a doped system with highd-electron occupancy, rather than low occupancy. Here, we report the realization of the charge-ordered phase in electron-doped (100) SrTiO₃epitaxial thin films that have the lowestd-electron occupancy i.e.,d¹-d⁰. Theoretical calculation predicts the presence of a metastable CO state in the bulk state of electron-doped SrTiO₃. Atomic scale analysis reveals that (100) surface distortion favors electron-lattice coupling for the charge-ordered state, and triggering the stabilization of the CO phase from a correlated metal state. This stabilization extends up to six unit cells from the top surface to the interior. Our approach offers an insight into the means of stabilizing a new phase of matter, extending CO phase to the lowest electron occupancy and encompassing a wide range of 3dtransition metal oxides. 

Classification is an important statistical tool that has increased its importance since the emergence of the data science revolution. However, a training data set that does not capture all underlying population subgroups (or clusters) will result in biased estimates or misclassification. In this paper, we introduce a statistical and computational solution to a possible bias in classification when implemented on estimated population clusters. An unseen-cluster problem denotes the case in which the training data does not contain all underlying clusters in the population. Such a scenario may occur due to various reasons, such as sampling errors, selection bias, or emerging and disappearing population clusters. Once an unseen-cluster problem occurs, a testing observation will be misclassified because a classification rule based on the sample cannot capture a cluster not observed in the training data (sample). To overcome such issues, we suggest a two-stage classification method to ameliorate the unseen-cluster problem in classification. We suggest a test to identify the unseen-cluster problem and demonstrate the performance of the two-stage tailored classifier using simulations and a public data example. 

Alternative cleavage and polyadenylation within introns (intronic APA) generate shorter mRNA isoforms; however, their physiological significance remains elusive. In this study, we developed a comprehensive workflow to analyze intronic APA profiles using the mammalian target of rapamycin (mTOR)-regulated transcriptome as a model system. Our investigation revealed two contrasting effects within the transcriptome in response to fluctuations in cellular mTOR activity: an increase in intronic APA for a subset of genes and a decrease for another subset of genes. The application of this workflow to RNA-seq data from The Cancer Genome Atlas demonstrated that this dichotomous intronic APA pattern is a consistent feature in transcriptomes across both normal tissues and various cancer types. Notably, our analyses of protein length changes resulting from intronic APA events revealed two distinct phenomena in proteome programming: a loss of functional domains due to significant changes in protein length or minimal alterations in C- terminal protein sequences within unstructured regions. Focusing on conserved intronic APA events across 10 different cancer types highlighted the prevalence of the latter cases in cancer transcriptomes, whereas the former cases were relatively enriched in normal tissue transcriptomes. These observations suggest potential, yet distinct, roles for intronic APA events during pathogenic processes and emphasize the abundance of protein isoforms with similar lengths in the cancer proteome. Furthermore, our investigation into the isoform-specific functions of JMJD6 intronic APA events supported the hypothesis that alterations in unstructured C-terminal protein regions lead to functional differences. Collectively, our findings underscore intronic APA events as a discrete molecular signature present in both normal tissues and cancer transcriptomes, highlighting the contribution of APA to the multifaceted functionality of the cancer proteome.