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23rd Human Proteome Organization World Congress

Rapid, quantitative and in-depth proteome profiling of single cells

Keynote lecture

Single-cell and spatial proteomics workflows for in-depth tissue mapping

Keynote Lecture

The composition of heterogenous cell types and their tightly controlled spatial localization within tissues dictates health or disease. To characterize and map these cellular identities, recently developed workflows for single-cell or spatial proteomics have demonstrated unprecedented sensitivity. The key improvements relate to automated nanoliter sample preparation and use of latest generation mass spectrometry (MS)-instrumentation for reproducible proteome analysis of single mammalian cells or small tissue sections. Despite these advances, sample acquisition throughput at biologically relevant proteome depth to provide sufficient tissue representation remains challenging. To address these limitations, we have combined a dedicated nano-96-well chip to in-line sample clean-up and chromatographic separation. In conjunction with highly sensitive and robust MS acquisition, the workflow enables robust and highly sensitive low-input proteomics, including single cell and spatial tissue analysis. Using 40SPD on the Evosep with optimized low-input dia-PASEF methods on the timsTOF Ultra2 we identify and quantify more than 4,000 proteins per single cell in solution or 25 fresh-frozen cells. To preserve the spatial context of cells within tissues we additionally combine our proteoCHIP workflow with laser capture microdissection and excise regions of interest down to 25 FFPE or fresh-frozen cells. The combination of acquisition throughput (40SPD) with this surprising proteome depth enables us to build a spatially resolved tissue catalogue including diverse tumor types and normal adjacent tissue. Based on this integrative single cell and spatial workflow we will demonstrate the analysis of clinically relevant samples.

From discovery to clinic: deep visual proteomics for advanced precision medicine

Spatial and Imaging Proteomics

The individual architecture of tissue holds information that is invaluable for clinical diagnosis and treatment. Molecular approaches such as genomics consult the pathological assessment of patient specimens, but often fall short owing to nonlinear genomic-phenotypic relationships. Immuno-based imaging of tissue sections provides spatial resolution of a phenotype by targeting specific proteins but lacks information depth and requires a-priori knowledge on the analysis outcome. Correlating the visual and proteomic phenotype of single cells while maintaining their localization in the native tissue landscape is hence key to improving precision medicine.We aim to explore disease-causing proteins and to identify druggable targets in individual patients. To this end, we employed a robust and scalable adaptation of the Deep Visual Proteomics (DVP) technology. DVP integrates high-content imaging, AI-based image analysis, single-cell isolation via high-precision laser microdissection and ultra-sensitive mass spectrometry-based proteomics. By employing cutting-edge artificial intelligence models, our platform enables accurate segmentation and classification of diverse cell types within tissue samples. This advanced phenotyping approach facilitates the automated identification of morphologically distinct regions of interest within each tissue, laying the foundation for targeted analysis and discovery of novel therapeutic targets. Integrating laser microdissection (LMD7, Leica Microsystems) for precise single-cell isolation from minute tissue samples with advanced mass spectrometry (Astral, ThermoFisher) and standardized liquid chromatography (Evosep), our platform achieves a throughput of 80 samples per day, yielding in-depth proteomes of over 6,000 proteins from single-cell isolated phenotypes across diverse diseases and tissues. The generation of proteomic maps based on the original tissue specimens resolves distinct cell types or cell states within AI-annotated regions that represent the phenotypic heterogeneity of a given disease. We demonstrated the versatility and effectiveness of our platform by applying our spatial approach to complex oncology cases across various indications.In summary, our approach enables robust and scalable spatial proteomics to identify cell-type specific, druggable protein targets across diverse indications. This resolves the cellular heterogeneity of a disease in synergy with clinical pathology and presents clinicians with disease-relevant therapeutic options at the individual level. The aim of precision medicine is to match the right drug to the right patient. Using our spatial proteomic approach, we translate recent technological advances from discovery to the clinic, identifying novel and druggable protein targets.

Oral presentation

Global analysis of protein turnover dynamics in single cells during stem cell differentiation

New Technology: MS-based Proteomics

Even with recent improvements in sample preparation and instrumentation, single-cell proteomics (SCP) analyses mostly measure protein abundances, making the field unidimensional. In this study, we employ a pulse stable isotope labelling by amino acids in cell culture (pSILAC) approach to evaluate protein abundance and turnover in single cells (SC-pSILAC). We demonstrate that two SILAC labels are detectable from ~4000 proteins in single HeLa cells, using state-of-the-art SCP workflow, providing known biological information about protein turnover in HeLa cells and making pulsed and multiplex SILAC applicable to single-cell analysis. Then, we investigate drug-altering global and specific proteins turnover and highlight those treatments effects in single cells. Lastly, we perform a large-scale time-series SC-pSILAC analysis of human induced pluripotent stem cells undirected differentiation encompassing six sampling times over two months and &gt; 1000 cells. Abundance measurements highlight cell-specific markers of stem cells and cells from various lineages and organs. Turnover dynamics highlights differentiation-specific turnover of proteins and co-regulation of core members of protein complexes over the differentiation time. Finally, we show that histone turnover can highlight dividing and non-dividing cells with potential in stem cell and cancer research. Our study represents the most comprehensive SCP analysis to date, offering new insights into cellular diversity and pioneering functional measurements beyond protein abundance, distinguishing SCP from other single-cell omics methods and enhancing its scientific relevance in biological research in a multidimensional manner.

Professor

In-depth and high-throughput single-cell proteomics workflow defines function subtypes

Cell Biology Insights

The cell, the fundamental unit of life, is crucial for understanding the molecular mechanisms and functions of organisms in both healthy and diseased states. Mass spectrometry-based single‐cell proteomics aims to characterize biological functions and heterogeneity at the level of proteins in an unbiased manner. Despite its clear potential, single-cell proteomics faces formidable challenges in sensitivity and throughput. Here we develop a single-cell workflow for consistent, high-throughput and in-depth proteomic data acquisition. Our approach integrates an optimized oil-free workflow on a nanoliter dispensing device (cellenONE) with the Orbitrap Astral mass spectrometer (Thermo Scientific), effectively streamlining the proteomic acquisition process. By implementing the Whisper Zoom technology (Evosep), we increased the throughput to 80 or 120 samples per day (SPD). We also optimized a data-independent acquisition method with variable windows to enhance proteomic depth and quantitative consistency. This approach allowed us to achieved a proteomic depth of over 3,000 to 5,000 protein groups depending on cell size. The integration of multiplex-DIA (mDIA) can further multiply throughput by at least two-fold to 160 or 240 cells per day. Compared to single-cell RNA sequencing, single-cell proteomics exhibits lower variability and enables the definition of various cell states. We applied this refined workflow to a range of biological questions. For instance, in the study of melanoma single cells, we successfully characterized the proteomic distinctions between treatment resistant "persister cells" and a control population of cells. We achieved a proteomic depth of about 5,000 protein groups with high data completeness, even covering many important transcription factors. We identified elevated signatures like epithelial-mesenchymal transition, glycolysis metabolism and hypoxia in the persister cells, compared to cell cycle and DNA repair terms in the control melanoma cells. Our study demonstrates that single-cell proteomics can be performed with high throughput and significant proteomic depth, a depth that was state of the art for bulk proteomes not long ago. We also highlight the potential of single-cell proteomics in addressing complex biological questions and advancing our understanding of cellular heterogeneity.

Plenary hall

Session

Single cell analysis in biology and medicine

Session
Single cell analysis in biology and medicine

Termin

Vorsitzende

Programm

SessionSingle cell analysis in biology and medicine

Termin

Vorsitzende

Programm

Session
Single cell analysis in biology and medicine