automRm: An R Package for Fully Automatic LC-QQQ-MS Data Preprocessing Powered by Machine Learning

Recent Developments in Machine Learning for Mass Spectrometry

Statistical analysis and modeling of mass spectrometry (MS) data have a long and rich history with several modern MS-based applications using statistical and chemometric methods. Recently, machine learning (ML) has experienced a renaissance due to advents in computational hardware and the development of new algorithms for artificial neural networks (ANN) and deep learning architectures. Moreover, recent successes of new ANN and deep learning architectures in several areas of science, engineering, and society have further strengthened the ML field. Importantly, modern ML methods and architectures have enabled new approaches for tasks related to MS that are now widely adopted in several popular MS-based subdisciplines, such as mass spectrometry imaging and proteomics. Herein, we aim to provide an introductory summary of the practical aspects of ML methodology relevant to MS. Additionally, we seek to provide an up-to-date review of the most recent developments in ML integration with MS-based techniques while also providing critical insights into the future direction of the field.

Medcheck: a novel software for automated de-formulation of traditional Chinese medicine (TCM) prescriptions by liquid chromatography-mass spectrometry

Image 1.

Systematic Evaluation of Chromatographic Peak Quality for Targeted Mass Spectrometry via Variational Autoencoder

Targeted mass spectrometry is a powerful technique for quantifying specific proteins or metabolites in complex biological samples. Accurate peak picking is a critical step as it determines the absolute abundance of each analyte by integrating the area under the picked peaks. Although automated software exists for handling such complex tasks, manual intervention is often required to rectify potential errors like misclassification or mis-picking events, which can significantly affect quantification accuracy. Therefore, it is necessary to develop objective scoring functions to evaluate peak-picking results and to identify problematic cases for further inspection. In this study, we present targeted mass spectrometry quality encoder (TMSQE), a data-driven scoring function that summarizes peak quality in three types: transition level, peak group level, and consistency level across samples. Through unsupervised learning from large data sets containing 1,703,827 peak groups, TMSQE establishes a reliable standard for systematic and objective evaluations of chromatographic peak quality in targeted mass spectrometry. TMSQE shows a high degree of consistency with expert experiences and can efficiently capture problematic cases after the automated software. Furthermore, we demonstrate the generalizability of TMSQE by successfully applying it to various data sets, including both peptide and metabolite data sets. Our proposed scoring approach provides a reliable solution for consistent and accurate peak quality evaluation, facilitating peak quality control for targeted mass spectrometry.

Prostaglandin E2 controls the metabolic adaptation of T cells to the intestinal microenvironment

Immune cells must adapt to different environments during the course of an immune response. Here we study the adaptation of CD8+ T cells to the intestinal microenvironment and how this process shapes the establishment of the CD8+ T cell pool. CD8+ T cells progressively remodel their transcriptome and surface phenotype as they enter the gut wall, and downregulate expression of mitochondrial genes. Human and mouse intestinal CD8+ T cells have reduced mitochondrial mass, but maintain a viable energy balance to sustain their function. We find that the intestinal microenvironment is rich in prostaglandin E2 (PGE2), which drives mitochondrial depolarization in CD8+ T cells. Consequently, these cells engage autophagy to clear depolarized mitochondria, and enhance glutathione synthesis to scavenge reactive oxygen species (ROS) that result from mitochondrial depolarization. Impairing PGE2 sensing promotes CD8+ T cell accumulation in the gut, while tampering with autophagy and glutathione negatively impacts the T cell pool. Thus, a PGE2-autophagy-glutathione axis defines the metabolic adaptation of CD8+ T cells to the intestinal microenvironment, to ultimately influence the T cell pool.

HeapMS: An Automatic Peak-Picking Pipeline for Targeted Proteomic Data Powered by 2D Heatmap Transformation and Convolutional Neural Networks

The process of peak picking and quality assessment for multiple reaction monitoring (MRM) data demands significant human effort, especially for signals with low abundance and high interference. Although multiple peak-picking software packages are available, they often fail to detect peaks with low quality and do not report cases with low confidence. Furthermore, visual examination of all chromatograms is still necessary to identify uncertain or erroneous cases. This study introduces HeapMS, a web service that uses artificial intelligence to assist with peak picking and the quality assessment of MRM chromatograms. HeapMS applies a rule-based filter to remove chromatograms with low interference and high-confidence peak boundaries detected by Skyline. Additionally, it transforms two histograms (representing light and heavy peptides) into a single encoded heatmap and performs a two-step evaluation (quality detection and peak picking) using image convolutional neural networks. HeapMS offers three categories of peak picking: uncertain peak picking that requires manual inspection, deletion peak picking that requires removal or manual re-examination, and automatic peak picking. HeapMS acquires the chromatogram and peak-picking boundaries directly from Skyline output. The output results are imported back into Skyline for further manual inspection, facilitating integration with Skyline. HeapMS offers the benefit of detecting chromatograms that should be deleted or require human inspection. Based on defined categories, it can significantly reduce human workload and provide consistent results. Furthermore, by using heatmaps instead of histograms, HeapMS can adapt to future updates in image recognition models. The HeapMS is available at: https://github.com/ccllabe/HeapMS.

Prostaglandin E 2 controls the metabolic adaptation of T cells to the intestinal microenvironment

Immune cells must adapt to different environments during the course of an immune response. We studied the adaptation of CD8 + T cells to the intestinal microenvironment and how this process shapes their residency in the gut. CD8 + T cells progressively remodel their transcriptome and surface phenotype as they acquire gut residency, and downregulate expression of mitochondrial genes. Human and mouse gut-resident CD8 + T cells have reduced mitochondrial mass, but maintain a viable energy balance to sustain their function. We found that the intestinal microenvironment is rich in prostaglandin E 2 (PGE 2 ), which drives mitochondrial depolarization in CD8 + T cells. Consequently, these cells engage autophagy to clear depolarized mitochondria, and enhance glutathione synthesis to scavenge reactive oxygen species (ROS) that result from mitochondrial depolarization. Impairing PGE 2 sensing promotes CD8 + T cell accumulation in the gut, while tampering with autophagy and glutathione negatively impacts the T cell population. Thus, a PGE 2 -autophagy-glutathione axis defines the metabolic adaptation of CD8 + T cells to the intestinal microenvironment, to ultimately influence the T cell pool.

LC-MS-Based Targeted Metabolomics for FACS-Purified Rare Cells

Metabolism plays a fundamental role in regulating cellular functions and fate decisions. Liquid chromatography-mass spectrometry (LC-MS)-based targeted metabolomic approaches provide high-resolution insights into the metabolic state of a cell. However, the typical sample size is in the order of 10⁵-10⁷ cells and thus not compatible with rare cell populations, especially in the case of a prior flow cytometry-based purification step. Here, we present a comprehensively optimized protocol for targeted metabolomics on rare cell types, such as hematopoietic stem cells and mast cells. Only 5000 cells per sample are required to detect up to 80 metabolites above background. The use of regular-flow liquid chromatography allows for robust data acquisition, and the omission of drying or chemical derivatization avoids potential sources of error. Cell-type-specific differences are preserved while the addition of internal standards, generation of relevant background control samples, and targeted metabolite with quantifiers and qualifiers ensure high data quality. This protocol could help numerous studies to gain thorough insights into cellular metabolic profiles and simultaneously reduce the number of laboratory animals and the time-consuming and costly experiments associated with rare cell-type purification.

Artificial Intelligence Applications in Clinical Chemistry

Artificial intelligence (AI) applications are an area of active investigation in clinical chemistry. Numerous publications have demonstrated the promise of AI across all phases of testing including preanalytic, analytic, and postanalytic phases; this includes novel methods for detecting common specimen collection errors, predicting laboratory results and diagnoses, and enhancing autoverification workflows. Although AI applications pose several ethical and operational challenges, these technologies are expected to transform the practice of the clinical chemistry laboratory in the near future.