Sophia C. Mädler

Single-cell proteomics by mass spectrometry is emerging as a powerful and unbiased method for the characterization of biological heterogeneity. So far, it has been limited to cultured cells, whereas an expansion of the method to complex tissues would greatly enhance biological insights. Here we describe single-cell Deep Visual Proteomics (scDVP), a technology that integrates high-content imaging, laser microdissection and multiplexed mass spectrometry. scDVP resolves the context-dependent, spatial proteome of murine hepatocytes at a current depth of 1,700 proteins from a cell slice. Half of the proteome was differentially regulated in a spatial manner, with protein levels changing dramatically in proximity to the central vein. We applied machine learning to proteome classes and images, which subsequently inferred the spatial proteome from imaging data alone. scDVP is applicable to healthy and diseased tissues and complements other spatial proteomics and spatial omics technologies.

Tissue-resident memory T cells (T RM ) have recently emerged as crucial cellular players for host defense in a wide variety of tissues and barrier sites. Insights into the maintenance and regulatory checkpoints of human T RM cells remain scarce, especially due to the difficulties associated with tracking T cells through time and space in humans. We therefore sought to identify and characterize skin-resident T cells in humans defined by their long-term in situ lodgment. Allogeneic hematopoietic stem cell transplantation (allo-HSCT) preceded by myeloablative chemotherapy unmasked long-term sequestration of host T cell subsets in human skin despite complete donor T cell chimerism in the blood. Single-cell chimerism analysis paired with single-cell transcriptional profiling comprehensively characterized these bona fide long-term skin-resident T cells and revealed differential tissue maintenance for distinct T cell subsets, specific T RM cell markers such as galectin-3, but also tissue exit potential with retention of the transcriptomic T RM cell identity. Analysis of 26 allo-HSCT patients revealed profound interindividual variation in the tissue maintenance of host skin T cells. The long-term persistence of host skin T cells in a subset of these patients did not correlate with the development of chronic GvHD. Our data exemplify the power of exploiting a clinical situation as a proof of concept for the existence of bona fide human skin T RM cells and reveal long-term persistence of host T cells in a peripheral tissue but not in the circulation or bone marrow in a subset of allo-HSCT patients.

Abstract Protein misfolding diseases, including α1-antitrypsin deficiency (AATD), pose substantial health challenges, with their cellular progression still poorly understood 1–3 . We use spatial proteomics by mass spectrometry and machine learning to map AATD in human liver tissue. Combining Deep Visual Proteomics (DVP) with single-cell analysis 4,5 , we probe intact patient biopsies to resolve molecular events during hepatocyte stress in pseudotime across fibrosis stages. We achieve proteome depth of up to 4,300 proteins from one-third of a single cell in formalin-fixed, paraffin-embedded tissue. This dataset reveals a potentially clinically actionable peroxisomal upregulation that precedes the canonical unfolded protein response. Our single-cell proteomics data show α1-antitrypsin accumulation is largely cell-intrinsic, with minimal stress propagation between hepatocytes. We integrated proteomic data with artificial intelligence-guided image-based phenotyping across several disease stages, revealing a late-stage hepatocyte phenotype characterized by globular protein aggregates and distinct proteomic signatures, notably including elevated TNFSF10 (also known as TRAIL) amounts. This phenotype may represent a critical disease progression stage. Our study offers new insights into AATD pathogenesis and introduces a powerful methodology for high-resolution, in situ proteomic analysis of complex tissues. This approach holds potential to unravel molecular mechanisms in various protein misfolding disorders, setting a new standard for understanding disease progression at the single-cell level in human tissue.

Abstract Single-cell RNA sequencing (scRNA-seq) revolutionized our understanding of disease biology. The promise it presents to also transform translational research requires highly standardized and robust software workflows. Here, we present the toolkit Besca, which streamlines scRNA-seq analyses and their use to deconvolute bulk RNA-seq data according to current best practices. Beyond a standard workflow covering quality control, filtering, and clustering, two complementary Besca modules, utilizing hierarchical cell signatures and supervised machine learning, automate cell annotation and provide harmonized nomenclatures. Subsequently, the gene expression profiles can be employed to estimate cell type proportions in bulk transcriptomics data. Using multiple, diverse scRNA-seq datasets, some stemming from highly heterogeneous tumor tissue, we show how Besca aids acceleration, interoperability, reusability and interpretability of scRNA-seq data analyses, meeting crucial demands in translational research and beyond.