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Improvement of long-term outcomes through targeted treatment is a primary concern in kidney transplant medicine. Currently, the validation of a rejection diagnosis and subsequent treatment depends on the histological assessment of allograft biopsy samples, according to the Banff classification system. However, the lack of (early) disease-specific tissue markers hinders accurate diagnosis and thus timely intervention. This challenge mainly results from an incomplete understanding of the pathophysiological processes underlying late allograft failure. Integration of large-scale multimodal approaches for investigating allograft biopsy samples might offer new insights into this pathophysiology, which are necessary for the identification of novel therapeutic targets and the development of tailored immunotherapeutic interventions. Several omics technologies - including transcriptomic, proteomic, lipidomic and metabolomic tools (and multimodal data analysis strategies) - can be applied to allograft biopsy investigation. However, despite their successful application in research settings and their potential clinical value, several barriers limit the broad implementation of many of these tools into clinical practice. Among spatial-omics technologies, mass spectrometry imaging, which is under-represented in the transplant field, has the potential to enable multi-omics investigations that might expand the insights gained with current clinical analysis technologies., (© 2024. Springer Nature Limited.)

The use of mass spectrometry and antibody-based sequencing technologies at the single-cell level has led to an increase in single-cell proteomic datasets. Integrating these datasets is crucial to eliminate the batch effect that often arises due to their limited sequencing molecules. Although methods for horizontally integrating high-dimensional single-cell transcriptomic datasets can also be applied to single-cell proteomic datasets, a specialized approach explicitly tailored for low-dimensional proteomic datasets may enhance the integration process. Here, we introduce SCPRO-HI, an algorithm for the horizontal integration of antibody-based single-cell proteomic datasets. It utilizes a hierarchical cell anchoring technique to match cells based on the similarity of distinctive proteins for constituting cell clusters. A novel variational auto-encoder model is employed for correcting batch effects on the protein abundances, eliminating the need for mapping them into a new domain. Moreover, we propose a technique for extending the algorithm to high-dimensional datasets. The performance of the SCPRO-HI algorithm is evaluated using simulated and real-world single-cell proteomic datasets. The findings demonstrate our algorithm outperforms state-of-the-art methods, achieving a 75% higher silhouette score while preserving HVPs 13% better. Furthermore, the algorithm shows competitive performance in transcriptomic datasets, suggesting potential for integrating high-dimensional mass-spectrometry-based proteomic datasets., (© 2023 The Authors. PROTEOMICS published by Wiley‐VCH GmbH.)

The combined activity of epigenetic features, which include histone post-translational modifications, DNA methylation, and nucleosome positioning, regulates gene expression independently from changes in the DNA sequence, defining how the shared genetic information of an organism is used to generate different cell phenotypes. Alterations in epigenetic processes have been linked with a multitude of diseases, including cancer, fueling interest in the discovery of drugs targeting the proteins responsible for writing, erasing, or reading histone and DNA modifications. Mass spectrometry (MS)-based proteomics has emerged as a versatile tool that can assist drug discovery pipelines from target validation, through target deconvolution, to monitoring drug efficacy in vivo. Here, we provide an overview of the contributions of MS-based proteomics to epigenetic drug discovery, describing the main approaches that can be used to support different drug discovery pipelines and highlighting how they contributed to the development and characterization of epigenetic drugs., (© 2023 The Authors. Proteomics published by Wiley-VCH GmbH.)

In the last decade, single-cell RNA sequencing routinely performed on large numbers of single cells has greatly advanced our understanding of the underlying heterogeneity of complex biological systems. Technological advances have also enabled protein measurements, further contributing to the elucidation of cell types and states present in complex tissues. Recently, there have been independent advances in mass spectrometric techniques bringing us one step closer to characterizing single-cell proteomes. Here we discuss the challenges of detecting proteins in single cells by both mass spectrometry and sequencing-based methods. We review the state of the art for these techniques and propose that there is a space for technological advancements and complementary approaches that maximize the advantages of both classes of technologies., (© 2023. Springer Nature America, Inc.)

The concept of hepatocyte functional zonation is well established, with differences in metabolism and xenobiotic processing determined by multiple factors including oxygen and nutrient levels across the hepatic lobule. However, recent advances in single-cell genomics technologies, including single-cell and nuclei RNA sequencing, and the rapidly evolving fields of spatial transcriptomic and proteomic profiling have greatly increased our understanding of liver zonation. Here we discuss how these transformative experimental strategies are being leveraged to dissect liver zonation at unprecedented resolution and how this new information should facilitate the emergence of novel precision medicine-based therapies for patients with liver disease., (© 2022 The Authors. Hepatology published by Wiley Periodicals LLC on behalf of American Association for the Study of Liver Diseases.)

Methods for profiling RNA and protein expression in a spatially resolved manner are rapidly evolving, making it possible to comprehensively characterize cells and tissues in health and disease. To maximize the biological insights obtained using these techniques, it is critical to both clearly articulate the key biological questions in spatial analysis of tissues and develop the requisite computational tools to address them. Developers of analytical tools need to decide on the intrinsic molecular features of each cell that need to be considered, and how cell shape and morphological features are incorporated into the analysis. Also, optimal ways to compare different tissue samples at various length scales are still being sought. Grouping these biological problems and related computational algorithms into classes across length scales, thus characterizing common issues that need to be addressed, will facilitate further progress in spatial transcriptomics and proteomics., (© 2022. Springer Nature America, Inc.)