Human physiology and pathology arise from the coordinated interactions of diverse single cells. However, analyzing single cells has been limited by the low sensitivity and throughput of analytical methods. DNA sequencing has recently made such analysis feasible for nucleic acids, but single-cell protein analysis remains limited. Mass-spectrometry is the most powerful method for protein analysis, but its application to single cells faces three major challenges: Efficiently delivering proteins/peptides to MS detectors, identifying their sequences, and scaling the analysis to many thousands of single cells. These challenges have motivated corresponding solutions, including SCoPE-design multiplexing and clean, automated, and miniaturized sample preparation. Synergistically applied, these solutions enable quantifying thousands of proteins across many single cells and establish a solid foundation for further advances. Building upon this foundation, the SCoPE concept will enable analyzing subcellular organelles and post-translational modifications while increases in multiplexing capabilities will increase the throughput and decrease cost.
Mammalian cells have about 30,000-fold more protein molecules than mRNA molecules. This larger number of molecules and the associated larger dynamic range have major implications in the development of proteomics technologies. We examine these implications for both liquid chromatography-tandem mass spectrometry (LC-MS/MS) and single-molecule counting and provide estimates on how many molecules are routinely measured in proteomics experiments by LC-MS/MS. We review strategies that have been helpful for counting billions of protein molecules by LC-MS/MS and suggest that these strategies can benef…
Background: Trypanosoma brucei is the causative agent of human African sleeping sickness and Nagana in cattle. In addition to being an important pathogen T. brucei has developed into a model system in cell biology. Results: Using Stable Isotope Labelling of Amino acids in Cell culture (SILAC) in combination with mass spectrometry we determined the abundance of >1600 proteins in the long slender (LS), short stumpy (SS) mammalian bloodstream form stages relative to the procyclic (PC) insect-form stage. In total we identified 2645 proteins, corresponding to ~30% of the total proteome and for the …
regulation largely unexplored, in part due to methodological limitations. Indeed, we review evidence demonstrating that commonly used methods, such as transcriptomics, are inadequate because the variability in mRNAs coding for ribosomal proteins (RP) does not necessarily correspond to RP variability. Thus protein remodeling of ribosomes should be investigated by methods that allow direct quantification of RPs, ideally of isolated ribosomes. We review such methods, focusing on mass spectrometry and emphasizing method-specific biases and approaches to control these biases. We argue that using mu…
In recent years, cell-based assays have been frequently used in molecular interaction analysis. Cell-based assays complement traditional biochemical and biophysical methods, as they allow for molecular interaction analysis, mode of action studies and even drug screening processes to be performed under physiologically relevant conditions. In most cellular assays, biomolecules are usually labeled to achieve specificity. In order to overcome some of the drawbacks associated with label-based assays, we have recently introduced cell-based molography as a biosensor for the analysis of specific molec…
Imputing missing values is common practice in label-free quantitative proteomics. Imputation aims at replacing a missing value with a user-defined one. However, the imputation itself may not be optimally considered downstream of the imputation process, as imputed datasets are often considered as if they had always been complete. Hence, the uncertainty due to the imputation is not adequately taken into account. We provide a rigorous multiple imputation strategy, leading to a less biased estimation of the parameters' variability thanks to Rubin's rules. The imputation-based peptide's intensities…
University of Glasgow（格拉斯哥大学）；MBZUAI（穆罕默德·本·扎耶德人工智能大学）；Neurosurgery, Brigham and Women’s Hospital（布莱根和妇女医院神经外科）；University of New South Wales（新南威尔士大学）；Illinois Institute of Technology（伊利诺伊理工学院）；University of North Carolina at Chapel Hill（北卡罗来纳大学教堂山分校）；Harvard Medical School（哈佛医学院）
Liquid Chromatography coupled to Mass Spectrometry (LC-MS) based methods are commonly used for high-throughput, quantitative measurements of the proteome (i.e. the set of all proteins in a sample at a given time). Targeted LC-MS produces data in the form of a two-dimensional time series spectrum, with the mass to charge ratio of analytes (m/z) on one axis, and the retention time from the chromatography on the other. The elution of a peptide of interest produces highly specific patterns across multiple fragment ion traces (extracted ion chromatograms, or XICs). In this paper, we formulate this …
Inherited or produced? Inferring protein production kinetics when protein counts are shaped by a cell's division history
Center for Biological Physics, Arizona State University（亚利桑那州立大学生物物理中心）；Department of Physics, Arizona State University（亚利桑那州立大学物理系）；School of Molecular Sciences, Arizona State University（亚利桑那州立大学分子科学学院）
Harvard University（哈佛大学）；Broad Institute of MIT and Harvard（麻省理工学院和哈佛大学博德研究所）；Harvard Data Science（哈佛数据科学研究院）
Spatial context is central to understanding health and disease. Yet reference protein interaction networks lack such contextualization, thereby limiting the study of where protein interactions likely occur in the human body and how they may be altered in disease. Contextualized protein interactions could better characterize genes with disease-specific interactions and elucidate diseases' manifestation in specific cell types. Here, we introduce AWARE, a graph neural message passing approach to inject cellular and tissue context into protein embeddings. AWARE optimizes for a multi-scale embeddin…
Vector Institute for AI Research（向量人工智能研究所）；University of Toronto（多伦多大学）
Protein function is inherently linked to its localization within the cell, and fluorescent microscopy data is an indispensable resource for learning representations of proteins. Despite major developments in molecular representation learning, extracting functional information from biological images remains a non-trivial computational task. Current state-of-the-art approaches use autoencoder models to learn high-quality features by reconstructing images. However, such methods are prone to capturing noise and imaging artifacts. In this work, we revisit deep learning models used for classifying m…
Mining Temporal Patterns from iTRAQ Mass Spectrometry(LC-MS/MS) Data
大规模蛋白质组学分析正在成为生物学中的一项强大技术，并且在很大程度上依赖于由最先进的质谱仪获得的数据。与系统生物学中的任何其他领域一样，需要计算工具来处理这一数据海洋。iTRAQ（Isobaric Tags for Relative and Absolute quantification）是一种可同时定量多个样品中蛋白质的技术。虽然iTRAQ数据为生物学家提供了有用的见解，但由于其多重设计，执行分析和得出生物学结论更加复杂。一个这样的问题是找到在不同时间点之间以类似方式表现（即丰度变化）的蛋白质，因为蛋白质组学数据中的时间变化揭示了重要的生物信息。基于距离的方法，如欧氏距离或皮尔逊系数，和聚类技术，如k-均值等，是不能考虑到时间的信息的序列。在本文中，我们提出了一个线性时间算法聚类相似的模式之间的各种iTRAQ的时间过程数据，无论其绝对值。该算法被称为时间模式挖掘（TPM），将数据从笛卡尔平面映射到离散二进制平面。在映射之后，动态编程技术允许挖掘在时间上彼此更接近的类似数据元素。所提出的算法准确地聚类iTRAQ数据，时间上彼此接近，准确率超过99%。不同规模的问题的实验结果进行了分析，在质量的集群，执行时间和可扩展性的大型数据集。我们的蛋白质组学数据的一个例子是在年底提供展示的算法的性能和聚类时间序列的能力，而不管它们之间的距离。
