Ontology highlight
ABSTRACT:
PROVIDER: PRJNA1236904 | ENA |
REPOSITORIES: ENA
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Project description:The widespread adoption of single-cell proteomics (SCP) in biology has been limited by complex workflows and reliance on specialized instrumentation. Here we present EasySCP, a high-throughput and lossless method that integrates FACS-based single-cell sorting, an all-in-one, single-step digestion process in 384-well plates, and sensitive mass spectrometry. EasySCP identifies nearly 5,000 proteins from individual HEK293 cell. Applied to murine liver, EasySCP achieved spatially resolved proteomics profiling of hepatocytes zonation, detecting an average of 3,500 proteins per hepatocyte and uncovering zonation patterns for 3,277 out of 5,267 proteins, representing unprecedented depth and coverage in single-cell liver proteomics. Building on 215 conserved zonation markers, we further developed hepatocyte spatial status score (HSS) that enables accurately reconstruct liver zonation across single-cell and spatial multi-omics datasets. Together, our study introduces EasySCP, a broadly accessible tool for dissecting cellular heterogeneity at single-cell proteomics resolution in both healthy and disease states, effectively bridging the gap between transcriptomics and functional proteomics.
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Project description:The widespread adoption of single-cell proteomics (SCP) in biology has been limited by complex workflows and reliance on specialized instrumentation. Here we present EasySCP, a high-throughput and lossless method that integrates FACS-based single-cell sorting, an all-in-one, single-step digestion process in 384-well plates, and sensitive mass spectrometry. EasySCP identifies nearly 5,000 proteins from individual HEK293 cell. Applied to murine liver, EasySCP achieved spatially resolved proteomics profiling of hepatocytes zonation, detecting an average of 3,500 proteins per hepatocyte and uncovering zonation patterns for 3,277 out of 5,267 proteins, representing unprecedented depth and coverage in single-cell liver proteomics. Building on 215 conserved zonation markers, we further developed hepatocyte spatial status score (HSS) that enables accurately reconstruct liver zonation across single-cell and spatial multi-omics datasets. Together, our study introduces EasySCP, a broadly accessible tool for dissecting cellular heterogeneity at single-cell proteomics resolution in both healthy and disease states, effectively bridging the gap between transcriptomics and functional proteomics.
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Project description:The widespread adoption of single-cell proteomics (SCP) in biology has been limited by complex workflows and reliance on specialized instrumentation. Here we present EasySCP, a high-throughput and lossless method that integrates FACS-based single-cell sorting, an all-in-one, single-step digestion process in 384-well plates, and sensitive mass spectrometry. EasySCP identifies nearly 5,000 proteins from individual HEK293 cell. Applied to murine liver, EasySCP achieved spatially resolved proteomics profiling of hepatocytes zonation, detecting an average of 3,500 proteins per hepatocyte and uncovering zonation patterns for 3,277 out of 5,267 proteins, representing unprecedented depth and coverage in single-cell liver proteomics. Building on 215 conserved zonation markers, we further developed hepatocyte spatial status score (HSS) that enables accurately reconstruct liver zonation across single-cell and spatial multi-omics datasets. Together, our study introduces EasySCP, a broadly accessible tool for dissecting cellular heterogeneity at single-cell proteomics resolution in both healthy and disease states, effectively bridging the gap between transcriptomics and functional proteomics.
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Project description:Leiomyosarcoma is a smooth muscle–derived malignancy marked by significant clinical heterogeneity. The extent and nature of cellular heterogeneity and molecular underpinnings remain poorly understood. To address this at transcriptomic and epigenomic levels, we performed single-nucleus multiome sequencing on untreated primary leiomyosarcoma tissues. Malignant cells segregated almost exclusively into two previously unrecognized and epigenetically distinct states: a dedifferentiated, mesenchymal-like subtype (MES) and a differentiated smooth muscle–enriched subtype (SMC). Chromatin accessibility profiling revealed strong enrichment of nuclear factor I (NFI) transcription factor motifs in MES cells, whereas AP-1 family motifs—most prominently FOSL2—were selectively accessible in SMC cells. Established leiomyosarcoma cell lines faithfully recapitulated these subtypes, and targeted depletion of NFI or AP-1 factors suppressed proliferation, invasion, and in vivo tumor growth, demonstrating functional dependency on these transcriptional programs. Spatial transcriptomics across 328 tissue cores from 128 leiomyosarcomas showed that immunosuppressive macrophages preferentially cluster around MES regions, revealing a subtype-specific tumor–immune niche. Clinically, MES-dominant tumors were associated with significantly worse patient outcomes. Through an epigenetic inhibitor screen, we identify and validate SMARCA4/2 inhibition as a promising therapeutic vulnerability for MES leiomyosarcomas. Together, this work defines two epigenetically driven, transcription factor–regulated, and clinically relevant states of leiomyosarcoma, revealing mechanistic underpinnings of tumor heterogeneity and uncovering actionable therapeutic strategies.
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