Advancements in imaging technologies have revolutionized our ability to deeply profile pathological tissue architectures, generating large volumes of imaging data with unparalleled spatial resolution. This type of data collection, namely, spatial proteomics, offers invaluable insights into various human diseases. Simultaneously, computational algorithms have evolved to manage the increasing dimensionality of spatial proteomics inherent in this progress. Numerous imaging-based computational frameworks, such as computational pathology, have been proposed for research and clinical applications. However, the development of these fields demands diverse domain expertise, creating barriers to their integration and further application. This review seeks to bridge this divide by presenting a comprehensive guideline. We consolidate prevailing computational methods and outline a roadmap from image processing to data-driven, statistics-informed biomarker discovery. Additionally, we explore future perspectives as the field moves toward interfacing with other quantitative domains, holding significant promise for precision care in immuno-oncology.

It is generally believed that the regulation of gene expression involves protein translation occurring before RNA transcription. Therefore, it is crucial to investigate protein translation and its regulation. Recent advancements in biological sciences, particularly in the field of omics, have revolutionized protein translation research. These studies not only help characterize changes in protein translation during specific biological or pathological processes but also have significant implications in disease prevention and treatment. In this review, we summarize the latest methods in ribosome-based translation omics. We specifically focus on the application of fluorescence imaging technology and omics technology in studying overall protein translation. Additionally, we analyze the advantages, disadvantages, and application of these experimental methods, aiming to provide valuable insights and references to researchers studying translation.

Metals have a fundamental role in microbiology, and accurate methods are needed for their identification and quantification. The inability to assess cellular heterogeneity is considered an impediment to the successful treatment of different diseases. Unlike bulk approaches, single-cell analysis allows elemental heterogeneity across genetically identical populations to be related to specific biological events and to the effectiveness of drugs. Single particle-inductively coupled plasma-mass spectrometry (SP-ICP-MS) can analyse single cells in suspension and measure this heterogeneity. Here we explore advances in instrumental design, compare mass analysers and discuss key parameters requiring optimisation. This review has identified that the effect of pre-treatment of cell suspensions and cell fixation approaches require further study and novel validation methods are needed as using bulk measurements is unsatisfactory. SP-ICP-MS has the advantage that a large number of cells can be analysed; however, it does not provide spatial information. Techniques based on laser ablation (LA) enable elemental mapping at the single-cell level, such as laser-induced breakdown spectroscopy (LIBS) and laser ablation-inductively coupled plasma-mass spectrometry (LA-ICP-MS). The sensitivity of commercial LIBS instruments restricts its use for sub-tissue applications; however, the capacity to analyse endogenous bulk components paired with developments in nano-LIBS technology shows great potential for cellular research. LA-ICP-MS offers high sensitivity for the direct analysis of single cells, but standardisation requires further development. The hyphenation of these trace elemental analysis techniques and their coupling with multi-omic technologies for single-cell analysis have enormous potential in answering fundamental biological questions.

Multiple sclerosis (MS) is an inflammatory demyelinating and degenerative disease of the central nervous system (CNS). Although inflammatory responses are efficiently treated, therapies for progression are scarce and suboptimal, and biomarkers to predict the disease course are insufficient. Cure or preventive measures for MS require knowledge of core pathological events at the site of the tissue damage. Novelties in systems biology have emerged and paved the way for a more fine-grained understanding of key pathological pathways within the CNS, but they have also raised questions still without answers. Here, we systemically review the power of tissue and single-cell/nucleus CNS omics and discuss major gaps of integration into the clinical practice. Systemic search identified 49 transcriptome and 11 proteome studies of the CNS from 1997 till October 2021. Pioneering molecular discoveries indicate that MS affects the whole brain and all resident cell types. Despite inconsistency of results, studies imply increase in transcripts/proteins of semaphorins, heat shock proteins, myelin proteins, apolipoproteins and HLAs. Different lesions are characterized by distinct astrocytic and microglial polarization, altered oligodendrogenesis, and changes in specific neuronal subtypes. In all white matter lesion types, CXCL12, SCD, CD163 are highly expressed, and STAT6- and TGFβ-signaling are increased. In the grey matter lesions, TNF-signaling seems to drive cell death, and especially CUX2-expressing neurons may be susceptible to neurodegeneration. The vast heterogeneity at both cellular and lesional levels may underlie the clinical heterogeneity of MS, and it may be more complex than the current disease phenotyping in the clinical practice. Systems biology has not solved the mystery of MS, but it has discovered multiple molecules and networks potentially contributing to the pathogenesis. However, these results are mostly descriptive; focused functional studies of the molecular changes may open up for a better interpretation. Guidelines for acceptable quality or awareness of results from low quality data, and standardized computational and biological pipelines may help to overcome limited tissue availability and the \"snap shot\" problem of omics. These may help in identifying core pathological events and point in directions for focus in clinical prevention.

Bacteria establish complex, compositionally consistent communities on healthy leaves. Ecological processes such as dispersal, diversification, ecological drift, and selection as well as leaf surface physicochemistry and topology impact community assembly. Since the leaf surface is an oligotrophic environment, species interactions such as competition and cooperation may be major contributors to shape community structure. Furthermore, the plant immune system impacts on microbial community composition, as plant cells respond to bacterial molecules and shape their responses according to the mixture of molecules present. Such tunability of the plant immune network likely enables the plant host to differentiate between pathogenic and non-pathogenic colonisers, avoiding costly immune responses to non-pathogenic colonisers. Plant immune responses are either systemically distributed or locally confined, which in turn affects the colonisation pattern of the associated microbiota. However, how each of these factors impacts the bacterial community is unclear. To better understand this impact, bacterial communities need to be studied at a micrometre resolution, which is the scale that is relevant to the members of the community. Here, current insights into the driving factors influencing the assembly of leaf surface-colonising bacterial communities are discussed, with a special focus on plant host immunity as an emerging factor contributing to bacterial leaf colonisation.

Cell-cell and cell-matrix adhesions are fundamental in all multicellular organisms. They play a key role in cellular growth, differentiation, pattern formation and migration. Cell-cell adhesion is substantial in the immune response, pathogen-host interactions, and tumor development. The success of tissue engineering and stem cell implantations strongly depends on the fine control of live cell adhesion on the surface of natural or biomimetic scaffolds. Therefore, the quantitative and precise measurement of the adhesion strength of living cells is critical, not only in basic research but in modern technologies, too. Several techniques have been developed or are under development to quantify cell adhesion. All of them have their pros and cons, which has to be carefully considered before the experiments and interpretation of the recorded data. Current review provides a guide to choose the appropriate technique to answer a specific biological question or to complete a biomedical test by measuring cell adhesion.

Basic research in life science and medicine has dug into single cell level in recent years. Single-cell analysis offers to understand life from diverse perspectives and is used to profile cell heterogeneity to investigate mechanism of diseases. Single cell technologies have also found applications in forensic medicine and clinical reproductive medicine, while the techniques are rapidly evolving and have become more and more sophisticated. In this article, we reviewed various single cell isolation techniques and their pros and cons, including manual cell picking, laser capture microdissection and microfluidics, as well as analysis methods for DNA, RNA and protein in single cell. In addition, we summarized major up-to-date single cell research achievements and their potential applications.
近年来，生命科学和医学的基础研究已深入到单细胞阶段。单细胞研究为揭示生命活动的基本规律、探索细胞异质性、提高对疾病发病机制的认识等提供了重要的线索和依据，同时，单细胞技术已被应用于日常实践中，如法医学和临床生殖医学。单细胞研究中使用的技术也在不断变化，并越来越复杂。文中主要介绍单细胞分离技术，包括手工挑取、激光捕获显微切割和微流控技术，以及单细胞中DNA、RNA 和蛋白质分析方法的各种技术。此外，文中总结了近年来生命科学和医学领域的主要单细胞研究成果，讨论了单细胞相关技术和研究的不足，并介绍了其未来的发展方向。.

In practice, food products tend to be contaminated with food-borne pathogens at a low inoculum level. However, the huge potential risk cannot be ignored because microbes may initiate high-speed growth suitable conditions during the food chain, such as transportation or storage. Thus, it is important to perform predictive modeling of microbial single cells. Several key aspects of microbial single-cell modeling are covered in this review. First, based on previous studies, the techniques of microbial single-cell data acquisition and growth data collection are presented in detail. In addition, the sources of microbial single-cell variability are also summarized. Due to model microbial growth, traditional deterministic mathematical models have been developed. However, most models fail to make accurate predictions at low cell numbers or at the single-cell level due to high cell-to-cell heterogeneity. Stochastic models have been a subject of great interest; and these models take into consideration the variability in microbial single-cell behavior.

The use of genetically encoded fluorescent reporters allows speeding up the initial optimization steps of microbial bioprocesses. These reporters can be used for determining the expression level of a particular promoter, not only the synthesis of a specific protein but also the content of intracellular metabolites. The level of protein/metabolite is thus proportional to a fluorescence signal. By this way, mean expression profiles of protein/metabolites can be determined non-invasively at a high-throughput rate, allowing the rapid identification of the best producers. Actually, different kinds of reporter systems are available, as well as specific cultivation devices allowing the on-line recording of the fluorescent signal. Cell-to-cell variability is another important phenomenon that can be integrated into the screening procedures for the selection of more efficient microbial cell factories.

Over 3.8 billion years of evolution has enabled many microbial species a versatile metabolism. However, limited by experimental methods, some unique metabolism remains unknown or unclear. A major obstacle is to attribute the incorporation of certain nutrients into a noncultivable species out of a complex microbial community. Such difficulty could be solved if we are able to directly observe substrate uptake at the single-cell level. Nanoscale secondary ion mass spectrometry (NanoSIMS) is a powerful tool for revealing element distribution in nanometer-scale resolution in the fields such as material sciences, geosciences and astronomy. In this review, we focus on another applicability of NanoSIMS in microbiology. In such fields, physiological properties and metabolic activities of microorganisms can be revealed with a single-cell scale resolution by NanoSIMS solely or in combination with other techniques. This review will highlight the features of NanoSIMS in analyzing the metabolic activities of carbon, nitrogen, metal irons by mixed-culture assemblies. Some values of NanoSIMS in environmental microbiology are expected to be discussed via this review.