High-throughput fluorescence microscopic analysis of protein abundance and localization in budding yeast

Application of Machine Learning in Spatial Proteomics

Spatial proteomics is an interdisciplinary field that investigates the localization and dynamics of proteins, and it has gained extensive attention in recent years, especially the subcellular proteomics. Numerous evidence indicate that the subcellular localization of proteins is associated with various cellular processes and disease progression. Mass spectrometry (MS)-based and imaging-based experimental approaches have been developed to acquire large-scale spatial proteomic data. To allow the reliable analysis of increasingly complex spatial proteomics data, machine learning (ML) methods have been widely used in both MS-based and imaging-based spatial proteomic data analysis pipelines. Here, we comprehensively survey the applications of ML in spatial proteomics from following aspects: (1) data resources for spatial proteome are comprehensively introduced; (2) the roles of different ML algorithms in data analysis pipelines are elaborated; (3) successful applications of spatial proteomics and several analytical tools integrating ML methods are presented; (4) challenges existing in modern ML-based spatial proteomics studies are discussed. This review provides guidelines for researchers seeking to apply ML methods to analyze spatial proteomic data and can facilitate insightful understanding of cell biology as well as the future research in medical and drug discovery communities.

Quantitative Characterisation of Low Abundant Yeast Mitochondrial Proteins Reveals Compensation for Haplo-Insufficiency in Different Environments

The quantification of low abundant membrane-binding proteins such as transcriptional factors and chaperones has proven difficult, even with the most sophisticated analytical technologies. Here, we exploit and optimise the non-invasive Fluorescence Correlation Spectroscopy (FCS) for the quantitation of low abundance proteins, and as proof of principle, we choose two interacting proteins involved in the fission of mitochondria in yeast, Fis1p and Mdv1p. In Saccharomyces cerevisiae, the recruitment of Fis1p and Mdv1p to mitochondria is essential for the scission of the organelles and the retention of functional mitochondrial structures in the cell. We use FCS in single GFP-labelled live yeast cells to quantify the protein abundance in homozygote and heterozygote cells and to investigate the impact of the environments on protein copy number, bound/unbound protein state and mobility kinetics. Both proteins were observed to localise predominantly at mitochondrial structures, with the Mdv1p bound state increasing significantly in a strictly respiratory environment. Moreover, a compensatory mechanism that controls Fis1p abundance upon deletion of one allele was observed in Fis1p but not in Mdv1p, suggesting differential regulation of Fis1p and Mdv1p protein expression.

Subcellular Transcriptomics and Proteomics: A Comparative Methods Review

The internal environment of cells is molecularly crowded, which requires spatial organization via subcellular compartmentalization. These compartments harbor specific conditions for molecules to perform their biological functions, such as coordination of the cell cycle, cell survival, and growth. This compartmentalization is also not static, with molecules trafficking between these subcellular neighborhoods to carry out their functions. For example, some biomolecules are multifunctional, requiring an environment with differing conditions or interacting partners, and others traffic to export such molecules. Aberrant localization of proteins or RNA species has been linked to many pathological conditions, such as neurological, cancer, and pulmonary diseases. Differential expression studies in transcriptomics and proteomics are relatively common, but the majority have overlooked the importance of subcellular information. In addition, subcellular transcriptomics and proteomics data do not always colocate because of the biochemical processes that occur during and after translation, highlighting the complementary nature of these fields. In this review, we discuss and directly compare the current methods in spatial proteomics and transcriptomics, which include sequencing- and imaging-based strategies, to give the reader an overview of the current tools available. We also discuss current limitations of these strategies as well as future developments in the field of spatial -omics.

Absolute Quantification of TGF-β Signaling Proteins Using Quantitative Western Blot

Cell signaling governs the basic functions of cells by molecular interactions that involve of many proteins. The abundance of signaling proteins can directly influence cellular responses to external signal, contributing to cellular heterogeneity. Absolute quantification of proteins is important for modeling and understanding the complex signaling network. Here, we introduce how to measure the amount of TGF-β signaling proteins using quantitative immunoblotting. In addition, we discuss how to convert the measurements of protein abundance to the quantities of absolute molecules per cell. This method is generally applicable to the absolute quantification of other proteins.

Pinpointing Cell Identity in Time and Space

Mammalian cells display a broad spectrum of phenotypes, morphologies, and functional niches within biological systems. Our understanding of mechanisms at the individual cellular level, and how cells function in concert to form tissues, organs and systems, has been greatly facilitated by centuries of extensive work to classify and characterize cell types. Classic histological approaches are now complemented with advanced single-cell sequencing and spatial transcriptomics for cell identity studies. Emerging data suggests that additional levels of information should be considered, including the subcellular spatial distribution of molecules such as RNA and protein, when classifying cells. In this Perspective piece we describe the importance of integrating cell transcriptional state with tissue and subcellular spatial and temporal information for thorough characterization of cell type and state. We refer to recent studies making use of single cell RNA-seq and/or image-based cell characterization, which highlight a need for such in-depth characterization of cell populations. We also describe the advances required in experimental, imaging and analytical methods to address these questions. This Perspective concludes by framing this argument in the context of projects such as the Human Cell Atlas, and related fields of cancer research and developmental biology.

Spatial proteomics: a powerful discovery tool for cell biology

Protein subcellular localization is tightly controlled and intimately linked to protein function in health and disease. Capturing the spatial proteome - that is, the localizations of proteins and their dynamics at the subcellular level - is therefore essential for a complete understanding of cell biology. Owing to substantial advances in microscopy, mass spectrometry and machine learning applications for data analysis, the field is now mature for proteome-wide investigations of spatial cellular regulation. Studies of the human proteome have begun to reveal a complex architecture, including single-cell variations, dynamic protein translocations, changing interaction networks and proteins localizing to multiple compartments. Furthermore, several studies have successfully harnessed the power of comparative spatial proteomics as a discovery tool to unravel disease mechanisms. We are at the beginning of an era in which spatial proteomics finally integrates with cell biology and medical research, thereby paving the way for unbiased systems-level insights into cellular processes. Here, we discuss current methods for spatial proteomics using imaging or mass spectrometry and specifically highlight global comparative applications. The aim of this Review is to survey the state of the field and also to encourage more cell biologists to apply spatial proteomics approaches.

Incorporation of a unified protein abundance dataset into the Saccharomyces genome database

The identification and accurate quantitation of protein abundance has been a major objective of proteomics research. Abundance studies have the potential to provide users with data that can be used to gain a deeper understanding of protein function and regulation and can also help identify cellular pathways and modules that operate under various environmental stress conditions. One of the central missions of the Saccharomyces Genome Database (SGD; https://www.yeastgenome.org) is to work with researchers to identify and incorporate datasets of interest to the wider scientific community, thereby enabling hypothesis-driven research. A large number of studies have detailed efforts to generate proteome-wide abundance data, but deeper analyses of these data have been hampered by the inability to compare results between studies. Recently, a unified protein abundance dataset was generated through the evaluation of more than 20 abundance datasets, which were normalized and converted to common measurement units, in this case molecules per cell. We have incorporated these normalized protein abundance data and associated metadata into the SGD database, as well as the SGD YeastMine data warehouse, resulting in the addition of 56 487 values for untreated cells grown in either rich or defined media and 28 335 values for cells treated with environmental stressors. Abundance data for protein-coding genes are displayed in a sortable, filterable table on Protein pages, available through Locus Summary pages. A median abundance value was incorporated, and a median absolute deviation was calculated for each protein-coding gene and incorporated into SGD. These values are displayed in the Protein section of the Locus Summary page. The inclusion of these data has enhanced the quality and quantity of protein experimental information presented at SGD and provides opportunities for researchers to access and utilize the data to further their research.

A Critical and Comparative Review of Fluorescent Tools for Live-Cell Imaging

Fluorescent tools have revolutionized our ability to probe biological dynamics, particularly at the cellular level. Fluorescent sensors have been developed on several platforms, utilizing either small-molecule dyes or fluorescent proteins, to monitor proteins, RNA, DNA, small molecules, and even cellular properties, such as pH and membrane potential. We briefly summarize the impressive history of tool development for these various applications and then discuss the most recent noteworthy developments in more detail. Particular emphasis is placed on tools suitable for single-cell analysis and especially live-cell imaging applications. Finally, we discuss prominent areas of need in future fluorescent tool development-specifically, advancing our capability to analyze and integrate the plethora of high-content data generated by fluorescence imaging.

Bringing Functional Genomics into Focus

Styles et al. develop an optimized method that combines high-content microscopy and automated phenotypic analysis with genome-wide yeast genetics to identify genes in DNA damage repair.