A quadrupole time-of-flight mass spectrometer coupled with a trapped ion mobility spectrometry (timsTOF) operated in parallel accumulation-serial fragmentation (PASEF) mode has recently emerged as a platform capable of providing four dimensional (4D) features comprising of elution time, collision cross section (CCS), mass-to-charge ratio and intensity of peptides. The PASEF mode provides ∼100% ion sampling efficiency both in data-dependent acquisition (DDA) and data-independent acquisition (DIA) modes without sacrificing sensitivity. In addition, targeted measurements using PASEF integrated parallel reaction monitoring (PRM) mode have also been described. However, only limited number of studies have used timsTOF for analysis of clinical samples. Although Orbitrap mass spectrometers have been used for biomarker discovery from cerebrospinal fluid (CSF) in a variety of neurological diseases, these Orbitrap-derived datasets cannot readily be applied for driving experiments on timsTOF mass spectrometers. We generated a catalog of peptides and proteins in human CSF in DDA mode on a timsTOF mass spectrometer and used these data to build a spectral library. This strategy allowed us to use elution times and ion mobility values from the spectral library to design PRM experiments for quantifying previously discovered biomarkers from CSF samples in Alzheimer's disease. When the same samples were analyzed using a DIA approach combined with a spectral library search, a higher number of proteins were identified than in a library-free approach. Overall, we have established a spectral library of CSF as a resource and demonstrated its utility for PRM and DIA studies, which should facilitate studies of neurological disorders. This article is protected by copyright. All rights reserved.

The yak (Bos grunniens) is a species adapted to the hypoxic environment in the plateau area. The heart is a hypoxia-sensitive organ involved in this adaptation. Herein, we used single-cell RNA-seq technology and clustering to determine the presence of 11 cell populations in the yak heart. We analyzed gene expression differences and expression patterns in each cell subpopulation at different altitudes. The cells related to altitude changes are mainly smooth muscle cells and vascular endothelial cells. Of the four transcription factors (MEF2B, FOXP4, ARID5A, HES4) found in smooth muscle cells, only MEF2B was specifically expressed in vascular smooth muscle cells. Three key transcription factors (HNF1B, DMRTA1, ARNTL2) were also found in the cardiomyocyte module. Compared with data extracted from low-altitude yak, we observed that the high altitude yak has enhanced contraction and relaxation of vascular smooth muscle cells and an increased metabolic level of cardiomyocytes. These may be strategies for the yak to adapt to high-altitude hypoxia environments. This article is protected by copyright. All rights reserved.

The evolution of omics and computational competency has accelerated discoveries of the underlying biological processes in an unprecedented way. High throughput methodologies, such as flow cytometry, can reveal deeper insights into cell processes, thereby allowing opportunities for scientific discoveries related to health and diseases. However, working with cytometry data often imposes complex computational challenges due to high-dimensionality, large size, and nonlinearity of the data structure. In addition, cytometry data frequently exhibit diverse patterns across biomarkers and suffer from substantial class imbalances which can further complicate the problem. The existing methods of cytometry data analysis either predict cell population or perform feature selection. Through this study, we propose a "wisdom of the crowd" approach to simultaneously predict rare cell populations and perform feature selection by integrating a pool of modern machine learning (ML) algorithms. Given that our approach integrates superior performing ML models across different normalization techniques based on entropy and rank, our method can detect diverse patterns existing across the model features. Furthermore, the method identifies a dynamic biomarker structure that divides the features into persistently selected, unselected, and fluctuating assemblies indicating the role of each biomarker in rare cell prediction, which can subsequently aid in studies of disease progression.

In top-down (TD) proteomics, efficient proteoform separation is crucial to reduce the sample complexity and increase the depth of the analysis. Here, we developed a two-dimensional low pH/low pH reversed-phase liquid chromatography separation scheme for TD proteomics. The first dimension for offline fractionation was performed using a polymeric reversed-phase (PLRP-S) column with trifluoroacetic acid as ion-pairing reagent. The second dimension, a C4 nanocolumn with formic acid as ion-pairing reagent, was coupled online with a high-field asymmetric ion mobility spectrometry (FAIMS) Orbitrap Tribrid mass spectrometer. For both dimensions several parameters were optimized, such as the adaption of the LC gradients in the second dimension according to the elution time (i.e., fraction number) in the first dimension. Avoidance of elevated temperatures and prolonged exposure to acidic conditions minimized cleavage of acid labile aspartate-proline peptide bonds. Furthermore, a concatenation strategy was developed to reduce the total measurement time. We compared our low/low pH with a previously published high pH (C4, ammonium formate)/low pH strategy and found that both separation strategies led to complementary proteoform identifications, mainly below 20 kDa, with a higher number of proteoforms identified by the low/low pH separation. With the optimized separation scheme, more than 4900 proteoforms from 1250 protein groups were identified in Caco-2 cells.

Genetically and phenotypically identical immune cell populations can be highly heterogenous in terms of their immune functions and protein secretion profiles. The microfluidic chip-based single-cell highly multiplexed secretome proteomics enables characterization of cellular heterogeneity of immune responses at different cellular and molecular layers. Increasing evidence has demonstrated that polyfunctional T cells that simultaneously produce 2+ proteins per cell at the single-cell level are key effector cells that contribute to the development of potent and durable cellular immunity against pathogens and cancers. The functional proteomic technology offers a wide spectrum of cellular function assessment and can uniquely define highly polyfunctional cell subsets with cytokine signatures from live individual cells. This high-dimensional single-cell analysis provides deep dissection into functional heterogeneity and helps identify predictive biomarkers and potential correlates that are crucial for immunotherapeutic product design optimization and personalized immunotherapy development to achieve better clinical outcomes. This article is protected by copyright. All rights reserved.

Plant metabolites are mainly produced through chemical reactions catalysed by enzymes encoded in the genome. Mutations in enzyme-encoding or transcription factor-encoding genes can alter the metabolome by changing the enzyme's catalytic activity or abundance, respectively. Insertion of transposable elements into non-coding regions has also been reported to affect transcription and ultimately metabolite content. In addition to genetic mutations, transgenerational epigenetic variations have also been found to affect metabolic content by controlling transcription of metabolism related genes. However, the majority of cases reported so far, in which epigenetic mechanisms are associated to metabolism, are non-transgenerational and are triggered by developmental signals or environmental stress. Although, accumulating research has provided evidence of strong genetic control of the metabolome, epigenetic control has been largely untouched. Here, we provide a review of the genetic and epigenetic control of metabolism with a focus on epigenetics. We discuss both transgenerational and non-transgenerational epigenetic marks regulating metabolism as well as prospects of the field of metabolic control where intricate interactions between genetics and epigenetics are involved. This article is protected by copyright. All rights reserved.

Gram-negative bacteria produce outer membrane vesicles (OMVs) and contain bacterial cargo including nucleic acids and proteins. The proteome of OMVs can be altered by various factors including bacterial growth stage, growth conditions, and environmental factors. However, it is currently unknown if the mechanism of OMV biogenesis can determine their proteome. In this study, we examined whether the mechanisms of OMV biogenesis influenced the production and protein composition of Pseudomonas aeruginosa OMVs. OMVs were isolated from three Pseudomonas aeruginosa strains that produced OMVs either by budding alone, by explosive cell lysis, or by both budding and explosive cell lysis. We identified that the mechanism of OMV biogenesis dictated OMV quantity. Furthermore, global proteomic analysis comparing the proteome of OMVs to their parent bacteria showed significant differences in the identification of proteins in bacteria and OMVs. Finally, we determined that the mechanism of OMV biogenesis influenced the protein composition of OMVs, as OMVs released by distinct mechanisms of biogenesis differed significantly to one another in their proteome and functional enrichment analysis. Overall, our findings reveal that the mechanism of OMV biogenesis is a main factor that determines the OMV proteome which may affect their subsequent biological functions. This article is protected by copyright. All rights reserved.

Lumen formation and inflation are crucial steps for tubular organ morphogenesis, yet the underling mechanism remains largely unrevealed. Here, we applied 4D proteomics to screen the lumenogenesis-related proteins and revealed the biological pathways potentially that are involved in lumen inflation during notochord lumen formation in the ascidian Ciona savignyi. In total, 910 differentiated expressed proteins (DEPs) were identified before and after notochord lumen formation utilizing Mfuzz analysis. Those DEPs were grouped into four upregulated clusters based on their quantitative expression patterns; the functions of these proteins were enriched in protein metabolic and biosynthetic process, establishment of localization and vesicle-mediated transport. We analyzed the vesicle trafficking cluster and focused on the several vesicle transport hub proteins. In vivo function-deficient experiments showed that mutation of vesicle transport proteins resulted in an abnormal lumen in notochord development, demonstrating the crucial role of intracellular trafficking for lumen formation. Moreover, abundant extracellular matrix proteins were identified, the majority of which were predicted to be glycosylated proteins. Inhibition of glycosylation markedly reduced the lumen expansion rate in notochord cells, suggesting that protein glycosylation is essential for lumenogenesis. Overall, our study provides an invaluable resource and reveals the crucial mechanisms in lumen formation and expansion. This article is protected by copyright. All rights reserved.