1
|
Wu R, Veličković M, Burnum-Johnson KE. From single cell to spatial multi-omics: unveiling molecular mechanisms in dynamic and heterogeneous systems. Curr Opin Biotechnol 2024; 89:103174. [PMID: 39126877 DOI: 10.1016/j.copbio.2024.103174] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/03/2024] [Accepted: 07/02/2024] [Indexed: 08/12/2024]
Abstract
Single-cell multi-omics and spatial technology have been widely applied to biomedical studies and recently to environmental studies. The cell size detected by single-cell omics ranges from ∼2 µm (e.g., Bacillus subtilis) to ∼120 µm (e.g., human oocytes). Simultaneous detection of single-cell multi-omics is available to human and plant tissues while limited to microbial samples. Spatial technology enables mapping the detected biomolecules in situ. The recent advances in Matrix-Assisted Laser Desorption/Ionization-Mass Spectrometry Imaging and Micro/Nanodroplet Processing in One Pot for Trace Samples for the first time allow the application of spatial multi-omics in highly heterogeneous environmental samples composed of plants, fungi, and bacteria. We envision that these technologies will continue to advance our understanding of unique cell types, their developmental trajectory, and the intercellular signaling and interaction within biological samples.
Collapse
Affiliation(s)
- Ruonan Wu
- Earth and Biological Sciences Directorate, Pacific Northwest National Laboratory, Richland, WA, USA
| | - Marija Veličković
- Earth and Biological Sciences Directorate, Pacific Northwest National Laboratory, Richland, WA, USA
| | - Kristin E Burnum-Johnson
- Earth and Biological Sciences Directorate, Pacific Northwest National Laboratory, Richland, WA, USA.
| |
Collapse
|
2
|
Wu X, Yang X, Dai Y, Zhao Z, Zhu J, Guo H, Yang R. Single-cell sequencing to multi-omics: technologies and applications. Biomark Res 2024; 12:110. [PMID: 39334490 PMCID: PMC11438019 DOI: 10.1186/s40364-024-00643-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/29/2024] [Accepted: 08/17/2024] [Indexed: 09/30/2024] Open
Abstract
Cells, as the fundamental units of life, contain multidimensional spatiotemporal information. Single-cell RNA sequencing (scRNA-seq) is revolutionizing biomedical science by analyzing cellular state and intercellular heterogeneity. Undoubtedly, single-cell transcriptomics has emerged as one of the most vibrant research fields today. With the optimization and innovation of single-cell sequencing technologies, the intricate multidimensional details concealed within cells are gradually unveiled. The combination of scRNA-seq and other multi-omics is at the forefront of the single-cell field. This involves simultaneously measuring various omics data within individual cells, expanding our understanding across a broader spectrum of dimensions. Single-cell multi-omics precisely captures the multidimensional aspects of single-cell transcriptomes, immune repertoire, spatial information, temporal information, epitopes, and other omics in diverse spatiotemporal contexts. In addition to depicting the cell atlas of normal or diseased tissues, it also provides a cornerstone for studying cell differentiation and development patterns, disease heterogeneity, drug resistance mechanisms, and treatment strategies. Herein, we review traditional single-cell sequencing technologies and outline the latest advancements in single-cell multi-omics. We summarize the current status and challenges of applying single-cell multi-omics technologies to biological research and clinical applications. Finally, we discuss the limitations and challenges of single-cell multi-omics and potential strategies to address them.
Collapse
Affiliation(s)
- Xiangyu Wu
- Department of Urology, Nanjing Drum Tower Hospital, Affiliated Hospital of Medical School, Nanjing University, 321 Zhongshan Road, Nanjing, 210008, Jiangsu, China
| | - Xin Yang
- Department of Urology, Nanjing Drum Tower Hospital, Affiliated Hospital of Medical School, Nanjing University, 321 Zhongshan Road, Nanjing, 210008, Jiangsu, China
| | - Yunhan Dai
- Medical School, Nanjing University, Nanjing, China
| | - Zihan Zhao
- Department of Urology, Nanjing Drum Tower Hospital, Affiliated Hospital of Medical School, Nanjing University, 321 Zhongshan Road, Nanjing, 210008, Jiangsu, China
| | - Junmeng Zhu
- Department of Oncology, Nanjing Drum Tower Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing, China
| | - Hongqian Guo
- Department of Urology, Nanjing Drum Tower Hospital, Affiliated Hospital of Medical School, Nanjing University, 321 Zhongshan Road, Nanjing, 210008, Jiangsu, China.
| | - Rong Yang
- Department of Urology, Nanjing Drum Tower Hospital, Affiliated Hospital of Medical School, Nanjing University, 321 Zhongshan Road, Nanjing, 210008, Jiangsu, China.
| |
Collapse
|
3
|
Shen B, Pade LR, Nemes P. Data-Independent Acquisition Shortens the Analytical Window of Single-Cell Proteomics to Fifteen Minutes in Capillary Electrophoresis Mass Spectrometry. J Proteome Res 2024. [PMID: 39325989 DOI: 10.1021/acs.jproteome.4c00491] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/28/2024]
Abstract
Separation in single-cell mass spectrometry (MS) improves molecular coverage and quantification; however, it also elongates measurements, thus limiting analytical throughput to study large populations of cells. Here, we advance the speed of bottom-up proteomics by capillary electrophoresis (CE) high-resolution mass spectrometry (MS) for single-cell proteomics. We adjust the applied electrophoresis potential to readily control the duration of electrophoresis. On the HeLa proteome standard, shorter separation times curbed proteome detection using data-dependent acquisition (DDA) but not data-independent acquisition (DIA) on an Orbitrap analyzer. This DIA method identified 1161 proteins vs 401 proteins by the reference DDA within a 15 min effective separation from single HeLa-cell-equivalent (∼200 pg) proteome digests. Label-free quantification found these exclusively DIA-identified proteins in the lower domain of the concentration range, revealing sensitivity improvement. The approach also significantly advanced the reproducibility of quantification, where ∼76% of the DIA-quantified proteins had <20% coefficient of variation vs ∼43% by DDA. As a proof of principle, the method allowed us to quantify 1242 proteins in subcellular niches in a single, neural-tissue fated cell in the live Xenopus laevis (frog) embryo, including many canonical components of organelles. DIA integration enhanced throughput by ∼2-4 fold and sensitivity by a factor of ∼3 in single-cell (subcellular) CE-MS proteomics.
Collapse
Affiliation(s)
- Bowen Shen
- Department of Chemistry & Biochemistry, University of Maryland, College Park, Maryland 20742, United States
| | - Leena R Pade
- Department of Chemistry & Biochemistry, University of Maryland, College Park, Maryland 20742, United States
| | - Peter Nemes
- Department of Chemistry & Biochemistry, University of Maryland, College Park, Maryland 20742, United States
| |
Collapse
|
4
|
Kwon Y, Woo J, Yu F, Williams SM, Markillie LM, Moore RJ, Nakayasu ES, Chen J, Campbell-Thompson M, Mathews CE, Nesvizhskii AI, Qian WJ, Zhu Y. Proteome-scale tissue mapping using mass spectrometry based on label-free and multiplexed workflows. Mol Cell Proteomics 2024:100841. [PMID: 39307423 DOI: 10.1016/j.mcpro.2024.100841] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/10/2024] [Revised: 08/19/2024] [Accepted: 08/23/2024] [Indexed: 09/25/2024] Open
Abstract
Multiplexed bimolecular profiling of tissue microenvironment, or spatial omics, can provide deep insight into cellular compositions and interactions in healthy and diseased tissues. Proteome-scale tissue mapping, which aims to unbiasedly visualize all the proteins in a whole tissue section or region of interest, has attracted significant interest because it holds great potential to directly reveal diagnostic biomarkers and therapeutic targets. While many approaches are available, however, proteome mapping still exhibits significant technical challenges in both protein coverage and analytical throughput. Since many of these existing challenges are associated with mass spectrometry-based protein identification and quantification, we performed a detailed benchmarking study of three protein quantification methods for spatial proteome mapping, including label-free, TMT-MS2, and TMT-MS3. Our study indicates label-free method provided the deepest coverages of ∼3500 proteins at a spatial resolution of 50 μm and the highest quantification dynamic range, while TMT-MS2 method holds great benefit in mapping throughput at >125 pixels per day. The evaluation also indicates both label-free and TMT-MS2 provide robust protein quantifications in identifying differentially abundant proteins and spatially co-variable clusters. In the study of pancreatic islet microenvironment, we demonstrated deep proteome mapping not only enables the identification of protein markers specific to different cell types, but more importantly, it also reveals unknown or hidden protein patterns by spatial co-expression analysis.
Collapse
Affiliation(s)
- Yumi Kwon
- Environmental Molecular Sciences Laboratory, Pacific Northwest National Laboratory, Richland, WA 99354, United States
| | - Jongmin Woo
- Environmental Molecular Sciences Laboratory, Pacific Northwest National Laboratory, Richland, WA 99354, United States
| | - Fengchao Yu
- Department of Pathology, University of Michigan, Ann Arbor, MI 48109, United States
| | - Sarah M Williams
- Environmental Molecular Sciences Laboratory, Pacific Northwest National Laboratory, Richland, WA 99354, United States
| | - Lye Meng Markillie
- Environmental Molecular Sciences Laboratory, Pacific Northwest National Laboratory, Richland, WA 99354, United States
| | - Ronald J Moore
- Biological Sciences Division, Pacific Northwest National Laboratory, Richland, WA 99354, United States
| | - Ernesto S Nakayasu
- Biological Sciences Division, Pacific Northwest National Laboratory, Richland, WA 99354, United States
| | - Jing Chen
- Department of Pathology, Immunology and Laboratory Medicine, University of Florida, Gainesville, FL 32610, United States
| | - Martha Campbell-Thompson
- Department of Pathology, Immunology and Laboratory Medicine, University of Florida, Gainesville, FL 32610, United States
| | - Clayton E Mathews
- Department of Pathology, Immunology and Laboratory Medicine, University of Florida, Gainesville, FL 32610, United States
| | - Alexey I Nesvizhskii
- Department of Pathology, University of Michigan, Ann Arbor, MI 48109, United States; Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI 48109, United States
| | - Wei-Jun Qian
- Biological Sciences Division, Pacific Northwest National Laboratory, Richland, WA 99354, United States.
| | - Ying Zhu
- Department of Proteomic and Genomic Technologies, Genentech Inc., 1 DNA Way, South San Francisco, CA 94080, United States.
| |
Collapse
|
5
|
Liu J, Bao C, Zhang J, Han Z, Fang H, Lu H. Artificial intelligence with mass spectrometry-based multimodal molecular profiling methods for advancing therapeutic discovery of infectious diseases. Pharmacol Ther 2024; 263:108712. [PMID: 39241918 DOI: 10.1016/j.pharmthera.2024.108712] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2024] [Revised: 07/22/2024] [Accepted: 09/03/2024] [Indexed: 09/09/2024]
Abstract
Infectious diseases, driven by a diverse array of pathogens, can swiftly undermine public health systems. Accurate diagnosis and treatment of infectious diseases-centered around the identification of biomarkers and the elucidation of disease mechanisms-are in dire need of more versatile and practical analytical approaches. Mass spectrometry (MS)-based molecular profiling methods can deliver a wealth of information on a range of functional molecules, including nucleic acids, proteins, and metabolites. While MS-driven omics analyses can yield vast datasets, the sheer complexity and multi-dimensionality of MS data can significantly hinder the identification and characterization of functional molecules within specific biological processes and events. Artificial intelligence (AI) emerges as a potent complementary tool that can substantially enhance the processing and interpretation of MS data. AI applications in this context lead to the reduction of spurious signals, the improvement of precision, the creation of standardized analytical frameworks, and the increase of data integration efficiency. This critical review emphasizes the pivotal roles of MS based omics strategies in the discovery of biomarkers and the clarification of infectious diseases. Additionally, the review underscores the transformative ability of AI techniques to enhance the utility of MS-based molecular profiling in the field of infectious diseases by refining the quality and practicality of data produced from omics analyses. In conclusion, we advocate for a forward-looking strategy that integrates AI with MS-based molecular profiling. This integration aims to transform the analytical landscape and the performance of biological molecule characterization, potentially down to the single-cell level. Such advancements are anticipated to propel the development of AI-driven predictive models, thus improving the monitoring of diagnostics and therapeutic discovery for the ongoing challenge related to infectious diseases.
Collapse
Affiliation(s)
- Jingjing Liu
- School of Chinese Medicine, Hong Kong Traditional Chinese Medicine Phenome Research Center, State Key Laboratory of Environmental and Biological Analysis, Hong Kong Baptist University, Hong Kong 999077, China
| | - Chaohui Bao
- Shanghai Institute of Hematology, State Key Laboratory of Medical Genomics, National Research Center for Translational Medicine at Shanghai, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China
| | - Jiaxin Zhang
- School of Chinese Medicine, Hong Kong Traditional Chinese Medicine Phenome Research Center, State Key Laboratory of Environmental and Biological Analysis, Hong Kong Baptist University, Hong Kong 999077, China
| | - Zeguang Han
- Key Laboratory of Systems Biomedicine (Ministry of Education), Shanghai Center for Systems Biomedicine, Shanghai Jiao Tong University, Shanghai 200240, China.
| | - Hai Fang
- Shanghai Institute of Hematology, State Key Laboratory of Medical Genomics, National Research Center for Translational Medicine at Shanghai, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China.
| | - Haitao Lu
- School of Chinese Medicine, Hong Kong Traditional Chinese Medicine Phenome Research Center, State Key Laboratory of Environmental and Biological Analysis, Hong Kong Baptist University, Hong Kong 999077, China; Shanghai Institute of Hematology, State Key Laboratory of Medical Genomics, National Research Center for Translational Medicine at Shanghai, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China; Key Laboratory of Systems Biomedicine (Ministry of Education), Shanghai Center for Systems Biomedicine, Shanghai Jiao Tong University, Shanghai 200240, China.
| |
Collapse
|
6
|
Cobley JN. Exploring the unmapped cysteine redox proteoform landscape. Am J Physiol Cell Physiol 2024; 327:C844-C866. [PMID: 39099422 DOI: 10.1152/ajpcell.00152.2024] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/07/2024] [Revised: 07/16/2024] [Accepted: 07/16/2024] [Indexed: 08/06/2024]
Abstract
Cysteine redox proteoforms define the diverse molecular states that proteins with cysteine residues can adopt. A protein with one cysteine residue must adopt one of two binary proteoforms: reduced or oxidized. Their numbers scale: a protein with 10 cysteine residues must assume one of 1,024 proteoforms. Although they play pivotal biological roles, the vast cysteine redox proteoform landscape comprising vast numbers of theoretical proteoforms remains largely uncharted. Progress is hampered by a general underappreciation of cysteine redox proteoforms, their intricate complexity, and the formidable challenges that they pose to existing methods. The present review advances cysteine redox proteoform theory, scrutinizes methodological barriers, and elaborates innovative technologies for detecting unique residue-defined cysteine redox proteoforms. For example, chemistry-enabled hybrid approaches combining the strengths of top-down mass spectrometry (TD-MS) and bottom-up mass spectrometry (BU-MS) for systematically cataloguing cysteine redox proteoforms are delineated. These methods provide the technological means to map uncharted redox terrain. To unravel hidden redox regulatory mechanisms, discover new biomarkers, and pinpoint therapeutic targets by mining the theoretical cysteine redox proteoform space, a community-wide initiative termed the "Human Cysteine Redox Proteoform Project" is proposed. Exploring the cysteine redox proteoform landscape could transform current understanding of redox biology.
Collapse
Affiliation(s)
- James N Cobley
- School of Life Sciences, University of Dundee, Dundee, United Kingdom
| |
Collapse
|
7
|
Liu L, Chen A, Li Y, Mulder J, Heyn H, Xu X. Spatiotemporal omics for biology and medicine. Cell 2024; 187:4488-4519. [PMID: 39178830 DOI: 10.1016/j.cell.2024.07.040] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/20/2024] [Revised: 07/05/2024] [Accepted: 07/23/2024] [Indexed: 08/26/2024]
Abstract
The completion of the Human Genome Project has provided a foundational blueprint for understanding human life. Nonetheless, understanding the intricate mechanisms through which our genetic blueprint is involved in disease or orchestrates development across temporal and spatial dimensions remains a profound scientific challenge. Recent breakthroughs in cellular omics technologies have paved new pathways for understanding the regulation of genomic elements and the relationship between gene expression, cellular functions, and cell fate determination. The advent of spatial omics technologies, encompassing both imaging and sequencing-based methodologies, has enabled a comprehensive understanding of biological processes from a cellular ecosystem perspective. This review offers an updated overview of how spatial omics has advanced our understanding of the translation of genetic information into cellular heterogeneity and tissue structural organization and their dynamic changes over time. It emphasizes the discovery of various biological phenomena, related to organ functionality, embryogenesis, species evolution, and the pathogenesis of diseases.
Collapse
Affiliation(s)
| | - Ao Chen
- BGI Research, Shenzhen 518083, China
| | | | - Jan Mulder
- Department of Neuroscience, Karolinska Institute, Stockholm, Sweden
| | - Holger Heyn
- Centro Nacional de Análisis Genómico (CNAG), Barcelona, Spain
| | - Xun Xu
- BGI Research, Hangzhou 310030, China; BGI Research, Shenzhen 518083, China.
| |
Collapse
|
8
|
Cesnik A, Schaffer LV, Gaur I, Jain M, Ideker T, Lundberg E. Mapping the Multiscale Proteomic Organization of Cellular and Disease Phenotypes. Annu Rev Biomed Data Sci 2024; 7:369-389. [PMID: 38748859 PMCID: PMC11343683 DOI: 10.1146/annurev-biodatasci-102423-113534] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/23/2024]
Abstract
While the primary sequences of human proteins have been cataloged for over a decade, determining how these are organized into a dynamic collection of multiprotein assemblies, with structures and functions spanning biological scales, is an ongoing venture. Systematic and data-driven analyses of these higher-order structures are emerging, facilitating the discovery and understanding of cellular phenotypes. At present, knowledge of protein localization and function has been primarily derived from manual annotation and curation in resources such as the Gene Ontology, which are biased toward richly annotated genes in the literature. Here, we envision a future powered by data-driven mapping of protein assemblies. These maps can capture and decode cellular functions through the integration of protein expression, localization, and interaction data across length scales and timescales. In this review, we focus on progress toward constructing integrated cell maps that accelerate the life sciences and translational research.
Collapse
Affiliation(s)
- Anthony Cesnik
- Department of Bioengineering, Stanford University, Stanford, California, USA;
| | - Leah V Schaffer
- Department of Medicine, University of California San Diego, La Jolla, California, USA;
| | - Ishan Gaur
- Department of Bioengineering, Stanford University, Stanford, California, USA;
| | - Mayank Jain
- Department of Medicine, University of California San Diego, La Jolla, California, USA;
| | - Trey Ideker
- Departments of Computer Science and Engineering and Bioengineering, University of California San Diego, La Jolla, California, USA
- Department of Medicine, University of California San Diego, La Jolla, California, USA;
| | - Emma Lundberg
- Chan Zuckerberg Biohub, San Francisco, California, USA
- Science for Life Laboratory, School of Engineering Sciences in Chemistry, Biotechnology and Health, KTH Royal Institute of Technology, Stockholm, Sweden
- Department of Pathology, Stanford University, Palo Alto, California, USA
- Department of Bioengineering, Stanford University, Stanford, California, USA;
| |
Collapse
|
9
|
Leduc A, Xu Y, Shipkovenska G, Dou Z, Slavov N. Limiting the impact of protein leakage in single-cell proteomics. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.07.26.605378. [PMID: 39091738 PMCID: PMC11291177 DOI: 10.1101/2024.07.26.605378] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Indexed: 08/04/2024]
Abstract
Limiting artifacts during sample preparation can significantly increase data quality in single-cell proteomics experiments. Towards this goal, we characterize the impact of protein leakage by analyzing thousands of primary single cells that were prepared either fresh immediately after dissociation or cryopreserved and prepared at a later date. We directly identify permeabilized cells and use the data to define a signature for protein leakage. We use this signature to build a classifier for identifying damaged cells that performs accurately across cell types and species.
Collapse
Affiliation(s)
- Andrew Leduc
- Departments of Bioengineering, Biology, Chemistry and Chemical Biology, Single Cell Proteomics Center, Northeastern University, Boston, MA 02115, USA
| | - Yanxin Xu
- Center for Regenerative Medicine, Massachusetts General Hospital, Boston, MA, USA
| | - Gergana Shipkovenska
- Center for Regenerative Medicine, Massachusetts General Hospital, Boston, MA, USA
| | - Zhixun Dou
- Center for Regenerative Medicine, Massachusetts General Hospital, Boston, MA, USA
| | - Nikolai Slavov
- Departments of Bioengineering, Biology, Chemistry and Chemical Biology, Single Cell Proteomics Center, Northeastern University, Boston, MA 02115, USA
- Parallel Squared Technology Institute, Watertown, MA 02472, USA
| |
Collapse
|
10
|
Savoca M, Takemoto K, Hu J, Li L, Jacob Kendrick B, Zhong Z, Lemasters JJ. MitoTracker Red for isolation of zone-specific hepatocytes and characterization of hepatic sublobular metabolism. Biochem Biophys Res Commun 2024; 735:150457. [PMID: 39146811 DOI: 10.1016/j.bbrc.2024.150457] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/13/2024] [Revised: 07/22/2024] [Accepted: 07/25/2024] [Indexed: 08/17/2024]
Abstract
BACKGROUND The liver lobule is divided into three zones or regions: periportal (PP or Zone 1) that is highly oxidative and active in ureagenesis, pericentral (PC or Zone 3) that is more glycolytic, and midzonal (MZ or Zone 2) with intermediate characteristics. AIM Our goal was to isolate and metabolically characterize hepatocytes from specific sublobular zones. METHODS Mice were administered rhodamine123 (Rh123) or MitoTracker Red (MTR) prior to intravital imaging, liver fixation, or hepatocyte isolation. After in vivo MTR, hepatocytes were isolated and sorted based on MTR fluorescence intensity. Alternatively, E-cadherin (Ecad) and cytochrome P450 2E1 (CYP2E1) immunolabeling was performed in fixed liver slices. Ecad and CYP2E1 gene expression in sorted hepatocytes was assessed by qPCR. Oxygen consumption rates (OCR) of sorted hepatocytes were also assessed. RESULTS Multiphoton microscopy showed Rh123 and MTR fluorescence distributed zonally, decreasing from PP to PC in a flow-dependent fashion. In liver cross-sections, Ecad was expressed periportally and CYP2E1 pericentrally in association with high and low MTR labeling, respectively. Based on MTR fluorescence, hepatocytes were sorted into PP, MZ, and PC populations with PP and PC hepatocytes enriched in Ecad and CYP2E1, respectively. OCR of PP hepatocytes was ∼4 times that of PC hepatocytes. CONCLUSIONS MTR treatment in vivo delineates sublobular hepatic zones and can be used to sort hepatocytes zonally. PP hepatocytes have substantially greater OCR compared to PC and MZ. The results also indicate a sharp midzonal demarcation between hepatocytes with PP characteristics (Ecad) and those with PC features (CYP2E1). This new method to sort hepatocytes in a zone-specific fashion holds the potential to shed light on sublobular hepatocyte metabolism and regulatory pathways in health and disease.
Collapse
Affiliation(s)
- Matthew Savoca
- Department of Drug Discovery & Biomedical Sciences, Medical University of South Carolina, Charleston, SC 29425, USA
| | - Kenji Takemoto
- Department of Drug Discovery & Biomedical Sciences, Medical University of South Carolina, Charleston, SC 29425, USA
| | - Jiangting Hu
- Department of Drug Discovery & Biomedical Sciences, Medical University of South Carolina, Charleston, SC 29425, USA
| | - Li Li
- Department of Drug Discovery & Biomedical Sciences, Medical University of South Carolina, Charleston, SC 29425, USA
| | - B Jacob Kendrick
- Hollings Cancer Center, Medical University of South Carolina, Charleston, SC 29425, USA; Darby Children's Research Institute, Medical University of South Carolina, Charleston, SC 29425, USA
| | - Zhi Zhong
- Department of Drug Discovery & Biomedical Sciences, Medical University of South Carolina, Charleston, SC 29425, USA
| | - John J Lemasters
- Department of Drug Discovery & Biomedical Sciences, Medical University of South Carolina, Charleston, SC 29425, USA; Hollings Cancer Center, Medical University of South Carolina, Charleston, SC 29425, USA; Department of Biochemistry & Molecular Biology, Medical University of South Carolina, Charleston, SC 29425, USA.
| |
Collapse
|
11
|
Coulombe B, Chapleau A, Macintosh J, Durcan TM, Poitras C, Moursli YA, Faubert D, Pinard M, Bernard G. Towards a Treatment for Leukodystrophy Using Cell-Based Interception and Precision Medicine. Biomolecules 2024; 14:857. [PMID: 39062571 PMCID: PMC11274857 DOI: 10.3390/biom14070857] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/23/2024] [Revised: 07/12/2024] [Accepted: 07/14/2024] [Indexed: 07/28/2024] Open
Abstract
Cell-based interception and precision medicine is a novel approach aimed at improving healthcare through the early detection and treatment of diseased cells. Here, we describe our recent progress towards developing cell-based interception and precision medicine to detect, understand, and advance the development of novel therapeutic approaches through a single-cell omics and drug screening platform, as part of a multi-laboratory collaborative effort, for a group of neurodegenerative disorders named leukodystrophies. Our strategy aims at the identification of diseased cells as early as possible to intercept progression of the disease prior to severe clinical impairment and irreversible tissue damage.
Collapse
Affiliation(s)
- Benoit Coulombe
- Translational Proteomics Laboratory, Institut de Recherches Cliniques de Montréal, Montréal, QC H2W 1R7, Canada; (C.P.); (Y.A.M.); (M.P.)
- Department of Biochemistry and Molecular Medicine, Université de Montréal, Montréal, QC H3T 1A8, Canada
| | - Alexandra Chapleau
- Department of Neurology and Neurosurgery, Pediatrics and Human Genetics, McGill University, Montréal, QC H9X 3V9, Canada; (A.C.); (J.M.); (G.B.)
- Child Health and Human Development Program, Research Institute of the McGill University Health Centre, Montréal, QC H4A 3J1, Canada
- The Neuro’s Early Drug Discovery Unit (EDDU), McGill University, Montréal, QC H9X 3V9, Canada;
| | - Julia Macintosh
- Department of Neurology and Neurosurgery, Pediatrics and Human Genetics, McGill University, Montréal, QC H9X 3V9, Canada; (A.C.); (J.M.); (G.B.)
- Child Health and Human Development Program, Research Institute of the McGill University Health Centre, Montréal, QC H4A 3J1, Canada
| | - Thomas M. Durcan
- The Neuro’s Early Drug Discovery Unit (EDDU), McGill University, Montréal, QC H9X 3V9, Canada;
| | - Christian Poitras
- Translational Proteomics Laboratory, Institut de Recherches Cliniques de Montréal, Montréal, QC H2W 1R7, Canada; (C.P.); (Y.A.M.); (M.P.)
| | - Yena A. Moursli
- Translational Proteomics Laboratory, Institut de Recherches Cliniques de Montréal, Montréal, QC H2W 1R7, Canada; (C.P.); (Y.A.M.); (M.P.)
| | - Denis Faubert
- Mass Spectrometry and Proteomics Platform, Institut de Recherches Cliniques de Montréal, Montréal, QC H2W 1R7, Canada;
| | - Maxime Pinard
- Translational Proteomics Laboratory, Institut de Recherches Cliniques de Montréal, Montréal, QC H2W 1R7, Canada; (C.P.); (Y.A.M.); (M.P.)
| | - Geneviève Bernard
- Department of Neurology and Neurosurgery, Pediatrics and Human Genetics, McGill University, Montréal, QC H9X 3V9, Canada; (A.C.); (J.M.); (G.B.)
- Child Health and Human Development Program, Research Institute of the McGill University Health Centre, Montréal, QC H4A 3J1, Canada
- Department Specialized Medicine, Division of Medical Genetics, McGill University Health Centre, Montréal, QC H4A 3J1, Canada
| |
Collapse
|
12
|
Kwon Y, Woo J, Yu F, Williams SM, Markillie LM, Moore RJ, Nakayasu ES, Chen J, Campbell-Thompson M, Mathews CE, Nesvizhskii AI, Qian WJ, Zhu Y. Proteome-scale tissue mapping using mass spectrometry based on label-free and multiplexed workflows. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.03.04.583367. [PMID: 38496682 PMCID: PMC10942300 DOI: 10.1101/2024.03.04.583367] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/19/2024]
Abstract
Multiplexed bimolecular profiling of tissue microenvironment, or spatial omics, can provide deep insight into cellular compositions and interactions in healthy and diseased tissues. Proteome-scale tissue mapping, which aims to unbiasedly visualize all the proteins in a whole tissue section or region of interest, has attracted significant interest because it holds great potential to directly reveal diagnostic biomarkers and therapeutic targets. While many approaches are available, however, proteome mapping still exhibits significant technical challenges in both protein coverage and analytical throughput. Since many of these existing challenges are associated with mass spectrometry-based protein identification and quantification, we performed a detailed benchmarking study of three protein quantification methods for spatial proteome mapping, including label-free, TMT-MS2, and TMT-MS3. Our study indicates label-free method provided the deepest coverages of ~3500 proteins at a spatial resolution of 50 µm and the highest quantification dynamic range, while TMT-MS2 method holds great benefit in mapping throughput at >125 pixels per day. The evaluation also indicates both label-free and TMT-MS2 provide robust protein quantifications in identifying differentially abundant proteins and spatially co-variable clusters. In the study of pancreatic islet microenvironment, we demonstrated deep proteome mapping not only enables the identification of protein markers specific to different cell types, but more importantly, it also reveals unknown or hidden protein patterns by spatial co-expression analysis.
Collapse
Affiliation(s)
- Yumi Kwon
- Environmental Molecular Sciences Laboratory, Pacific Northwest National Laboratory, Richland, WA 99354, United States
| | - Jongmin Woo
- Environmental Molecular Sciences Laboratory, Pacific Northwest National Laboratory, Richland, WA 99354, United States
| | - Fengchao Yu
- Department of Pathology, University of Michigan, Ann Arbor, MI 48109, United States
| | - Sarah M. Williams
- Environmental Molecular Sciences Laboratory, Pacific Northwest National Laboratory, Richland, WA 99354, United States
| | - Lye Meng Markillie
- Environmental Molecular Sciences Laboratory, Pacific Northwest National Laboratory, Richland, WA 99354, United States
| | - Ronald J. Moore
- Biological Sciences Division, Pacific Northwest National Laboratory, Richland, WA 99354, United States
| | - Ernesto S. Nakayasu
- Biological Sciences Division, Pacific Northwest National Laboratory, Richland, WA 99354, United States
| | - Jing Chen
- Department of Pathology, Immunology and Laboratory Medicine, University of Florida, Gainesville, FL 32610, United States
| | - Martha Campbell-Thompson
- Department of Pathology, Immunology and Laboratory Medicine, University of Florida, Gainesville, FL 32610, United States
| | - Clayton E. Mathews
- Department of Pathology, Immunology and Laboratory Medicine, University of Florida, Gainesville, FL 32610, United States
| | - Alexey I. Nesvizhskii
- Department of Pathology, University of Michigan, Ann Arbor, MI 48109, United States
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI 48109, United States
| | - Wei-Jun Qian
- Biological Sciences Division, Pacific Northwest National Laboratory, Richland, WA 99354, United States
| | - Ying Zhu
- Department of Proteomic and Genomic Technologies, Genentech Inc., 1 DNA Way, South San Francisco, CA 94080, United States
| |
Collapse
|
13
|
Nitz AA, Giraldez Chavez JH, Eliason ZG, Payne SH. Are We There Yet? Assessing the Readiness of Single-Cell Proteomics to Answer Biological Hypotheses. J Proteome Res 2024. [PMID: 38981598 DOI: 10.1021/acs.jproteome.4c00091] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/11/2024]
Abstract
Single-cell analysis is an active area of research in many fields of biology. Measurements at single-cell resolution allow researchers to study diverse populations without losing biologically meaningful information to sample averages. Many technologies have been used to study single cells, including mass spectrometry-based single-cell proteomics (SCP). SCP has seen a lot of growth over the past couple of years through improvements in data acquisition and analysis, leading to greater proteomic depth. Because method development has been the main focus in SCP, biological applications have been sprinkled in only as proof-of-concept. However, SCP methods now provide significant coverage of the proteome and have been implemented in many laboratories. Thus, a primary question to address in our community is whether the current state of technology is ready for widespread adoption for biological inquiry. In this Perspective, we examine the potential for SCP in three thematic areas of biological investigation: cell annotation, developmental trajectories, and spatial mapping. We identify that the primary limitation of SCP is sample throughput. As proteome depth has been the primary target for method development to date, we advocate for a change in focus to facilitate measuring tens of thousands of single-cell proteomes to enable biological applications beyond proof-of-concept.
Collapse
Affiliation(s)
- Alyssa A Nitz
- Biology Department, Brigham Young University, Provo, Utah 84602, United States
| | | | - Zachary G Eliason
- Biology Department, Brigham Young University, Provo, Utah 84602, United States
| | - Samuel H Payne
- Biology Department, Brigham Young University, Provo, Utah 84602, United States
| |
Collapse
|
14
|
Ctortecka C, Clark NM, Boyle BW, Seth A, Mani DR, Udeshi ND, Carr SA. Automated single-cell proteomics providing sufficient proteome depth to study complex biology beyond cell type classifications. Nat Commun 2024; 15:5707. [PMID: 38977691 PMCID: PMC11231172 DOI: 10.1038/s41467-024-49651-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/24/2024] [Accepted: 06/14/2024] [Indexed: 07/10/2024] Open
Abstract
The recent technological and computational advances in mass spectrometry-based single-cell proteomics have pushed the boundaries of sensitivity and throughput. However, reproducible quantification of thousands of proteins within a single cell remains challenging. To address some of those limitations, we present a dedicated sample preparation chip, the proteoCHIP EVO 96 that directly interfaces with the Evosep One. This, in combination with the Bruker timsTOF demonstrates double the identifications without manual sample handling and the newest generation timsTOF Ultra identifies up to 4000 with an average of 3500 protein groups per single HEK-293T without a carrier or match-between runs. Our workflow spans 4 orders of magnitude, identifies over 50 E3 ubiquitin-protein ligases, and profiles key regulatory proteins upon small molecule stimulation. This study demonstrates that the proteoCHIP EVO 96-based sample preparation with the timsTOF Ultra provides sufficient proteome depth to study complex biology beyond cell-type classifications.
Collapse
Affiliation(s)
| | | | - Brian W Boyle
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | | | - D R Mani
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | | | - Steven A Carr
- Broad Institute of MIT and Harvard, Cambridge, MA, USA.
| |
Collapse
|
15
|
Ferrero R, Rainer PY, Rumpler M, Russeil J, Zachara M, Pezoldt J, van Mierlo G, Gardeux V, Saelens W, Alpern D, Favre L, Vionnet N, Mantziari S, Zingg T, Pitteloud N, Suter M, Matter M, Schlaudraff KU, Canto C, Deplancke B. A human omentum-specific mesothelial-like stromal population inhibits adipogenesis through IGFBP2 secretion. Cell Metab 2024; 36:1566-1585.e9. [PMID: 38729152 DOI: 10.1016/j.cmet.2024.04.017] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/12/2023] [Revised: 12/22/2023] [Accepted: 04/19/2024] [Indexed: 05/12/2024]
Abstract
Adipose tissue plasticity is orchestrated by molecularly and functionally diverse cells within the stromal vascular fraction (SVF). Although several mouse and human adipose SVF cellular subpopulations have by now been identified, we still lack an understanding of the cellular and functional variability of adipose stem and progenitor cell (ASPC) populations across human fat depots. To address this, we performed single-cell and bulk RNA sequencing (RNA-seq) analyses of >30 SVF/Lin- samples across four human adipose depots, revealing two ubiquitous human ASPC (hASPC) subpopulations with distinct proliferative and adipogenic properties but also depot- and BMI-dependent proportions. Furthermore, we identified an omental-specific, high IGFBP2-expressing stromal population that transitions between mesothelial and mesenchymal cell states and inhibits hASPC adipogenesis through IGFBP2 secretion. Our analyses highlight the molecular and cellular uniqueness of different adipose niches, while our discovery of an anti-adipogenic IGFBP2+ omental-specific population provides a new rationale for the biomedically relevant, limited adipogenic capacity of omental hASPCs.
Collapse
Affiliation(s)
- Radiana Ferrero
- Laboratory of Systems Biology and Genetics, Institute of Bioengineering, School of Life Sciences, Ecole Polytechnique Fédérale de Lausanne (EPFL), 1015 Lausanne, Switzerland; Swiss Institute of Bioinformatics, 1015 Lausanne, Switzerland
| | - Pernille Yde Rainer
- Laboratory of Systems Biology and Genetics, Institute of Bioengineering, School of Life Sciences, Ecole Polytechnique Fédérale de Lausanne (EPFL), 1015 Lausanne, Switzerland; Swiss Institute of Bioinformatics, 1015 Lausanne, Switzerland
| | - Marie Rumpler
- Laboratory of Systems Biology and Genetics, Institute of Bioengineering, School of Life Sciences, Ecole Polytechnique Fédérale de Lausanne (EPFL), 1015 Lausanne, Switzerland; Swiss Institute of Bioinformatics, 1015 Lausanne, Switzerland
| | - Julie Russeil
- Laboratory of Systems Biology and Genetics, Institute of Bioengineering, School of Life Sciences, Ecole Polytechnique Fédérale de Lausanne (EPFL), 1015 Lausanne, Switzerland
| | - Magda Zachara
- Laboratory of Systems Biology and Genetics, Institute of Bioengineering, School of Life Sciences, Ecole Polytechnique Fédérale de Lausanne (EPFL), 1015 Lausanne, Switzerland
| | - Joern Pezoldt
- Laboratory of Systems Biology and Genetics, Institute of Bioengineering, School of Life Sciences, Ecole Polytechnique Fédérale de Lausanne (EPFL), 1015 Lausanne, Switzerland; Swiss Institute of Bioinformatics, 1015 Lausanne, Switzerland
| | - Guido van Mierlo
- Laboratory of Systems Biology and Genetics, Institute of Bioengineering, School of Life Sciences, Ecole Polytechnique Fédérale de Lausanne (EPFL), 1015 Lausanne, Switzerland; Swiss Institute of Bioinformatics, 1015 Lausanne, Switzerland
| | - Vincent Gardeux
- Laboratory of Systems Biology and Genetics, Institute of Bioengineering, School of Life Sciences, Ecole Polytechnique Fédérale de Lausanne (EPFL), 1015 Lausanne, Switzerland; Swiss Institute of Bioinformatics, 1015 Lausanne, Switzerland
| | - Wouter Saelens
- Laboratory of Systems Biology and Genetics, Institute of Bioengineering, School of Life Sciences, Ecole Polytechnique Fédérale de Lausanne (EPFL), 1015 Lausanne, Switzerland; Swiss Institute of Bioinformatics, 1015 Lausanne, Switzerland
| | - Daniel Alpern
- Laboratory of Systems Biology and Genetics, Institute of Bioengineering, School of Life Sciences, Ecole Polytechnique Fédérale de Lausanne (EPFL), 1015 Lausanne, Switzerland; Swiss Institute of Bioinformatics, 1015 Lausanne, Switzerland
| | - Lucie Favre
- Department of Endocrinology, Diabetology and Metabolism, University Hospital of Lausanne (CHUV), 1011 Lausanne, Switzerland; Faculty of Biology and Medicine, University of Lausanne, Lausanne 1005, Switzerland
| | - Nathalie Vionnet
- Department of Endocrinology, Diabetology and Metabolism, University Hospital of Lausanne (CHUV), 1011 Lausanne, Switzerland; Faculty of Biology and Medicine, University of Lausanne, Lausanne 1005, Switzerland
| | - Styliani Mantziari
- Department of Visceral Surgery, University Hospital of Lausanne (CHUV), Lausanne 1011, Switzerland; Faculty of Biology and Medicine, University of Lausanne, Lausanne 1005, Switzerland
| | - Tobias Zingg
- Department of Visceral Surgery, University Hospital of Lausanne (CHUV), Lausanne 1011, Switzerland; Faculty of Biology and Medicine, University of Lausanne, Lausanne 1005, Switzerland
| | - Nelly Pitteloud
- Department of Endocrinology, Diabetology and Metabolism, University Hospital of Lausanne (CHUV), 1011 Lausanne, Switzerland; Faculty of Biology and Medicine, University of Lausanne, Lausanne 1005, Switzerland
| | - Michel Suter
- Department of Visceral Surgery, University Hospital of Lausanne (CHUV), Lausanne 1011, Switzerland; Faculty of Biology and Medicine, University of Lausanne, Lausanne 1005, Switzerland
| | - Maurice Matter
- Department of Visceral Surgery, University Hospital of Lausanne (CHUV), Lausanne 1011, Switzerland; Faculty of Biology and Medicine, University of Lausanne, Lausanne 1005, Switzerland
| | | | - Carles Canto
- Laboratory of Systems Biology and Genetics, Institute of Bioengineering, School of Life Sciences, Ecole Polytechnique Fédérale de Lausanne (EPFL), 1015 Lausanne, Switzerland; Swiss Institute of Bioinformatics, 1015 Lausanne, Switzerland
| | - Bart Deplancke
- Laboratory of Systems Biology and Genetics, Institute of Bioengineering, School of Life Sciences, Ecole Polytechnique Fédérale de Lausanne (EPFL), 1015 Lausanne, Switzerland; Swiss Institute of Bioinformatics, 1015 Lausanne, Switzerland.
| |
Collapse
|
16
|
Verheijen FWM, Tran TNM, Chang J, Broere F, Zaal EA, Berkers CR. Deciphering metabolic crosstalk in context: lessons from inflammatory diseases. Mol Oncol 2024; 18:1759-1776. [PMID: 38275212 PMCID: PMC11223610 DOI: 10.1002/1878-0261.13588] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/17/2023] [Revised: 11/02/2023] [Accepted: 01/15/2024] [Indexed: 01/27/2024] Open
Abstract
Metabolism plays a crucial role in regulating the function of immune cells in both health and disease, with altered metabolism contributing to the pathogenesis of cancer and many inflammatory diseases. The local microenvironment has a profound impact on the metabolism of immune cells. Therefore, immunological and metabolic heterogeneity as well as the spatial organization of cells in tissues should be taken into account when studying immunometabolism. Here, we highlight challenges of investigating metabolic communication. Additionally, we review the capabilities and limitations of current technologies for studying metabolism in inflamed microenvironments, including single-cell omics techniques, flow cytometry-based methods (Met-Flow, single-cell energetic metabolism by profiling translation inhibition (SCENITH)), cytometry by time of flight (CyTOF), cellular indexing of transcriptomes and epitopes by sequencing (CITE-Seq), and mass spectrometry imaging. Considering the importance of metabolism in regulating immune cells in diseased states, we also discuss the applications of metabolomics in clinical research, as well as some hurdles to overcome to implement these techniques in standard clinical practice. Finally, we provide a flowchart to assist scientists in designing effective strategies to unravel immunometabolism in disease-relevant contexts.
Collapse
Affiliation(s)
- Fenne W. M. Verheijen
- Division of Cell Biology, Metabolism & Cancer, Department Biomolecular Health Sciences, Faculty of Veterinary MedicineUtrecht UniversityThe Netherlands
- Division of Infectious Diseases and Immunology, Department Biomolecular Health Sciences, Faculty of Veterinary MedicineUtrecht UniversityThe Netherlands
| | - Thi N. M. Tran
- Division of Cell Biology, Metabolism & Cancer, Department Biomolecular Health Sciences, Faculty of Veterinary MedicineUtrecht UniversityThe Netherlands
- Biomolecular Mass Spectrometry and Proteomics, Bijvoet Centre for Biomolecular ResearchUtrecht UniversityThe Netherlands
| | - Jung‐Chin Chang
- Division of Cell Biology, Metabolism & Cancer, Department Biomolecular Health Sciences, Faculty of Veterinary MedicineUtrecht UniversityThe Netherlands
| | - Femke Broere
- Division of Infectious Diseases and Immunology, Department Biomolecular Health Sciences, Faculty of Veterinary MedicineUtrecht UniversityThe Netherlands
| | - Esther A. Zaal
- Division of Cell Biology, Metabolism & Cancer, Department Biomolecular Health Sciences, Faculty of Veterinary MedicineUtrecht UniversityThe Netherlands
| | - Celia R. Berkers
- Division of Cell Biology, Metabolism & Cancer, Department Biomolecular Health Sciences, Faculty of Veterinary MedicineUtrecht UniversityThe Netherlands
| |
Collapse
|
17
|
Ertürk A. Deep 3D histology powered by tissue clearing, omics and AI. Nat Methods 2024; 21:1153-1165. [PMID: 38997593 DOI: 10.1038/s41592-024-02327-1] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/24/2022] [Accepted: 05/28/2024] [Indexed: 07/14/2024]
Abstract
To comprehensively understand tissue and organism physiology and pathophysiology, it is essential to create complete three-dimensional (3D) cellular maps. These maps require structural data, such as the 3D configuration and positioning of tissues and cells, and molecular data on the constitution of each cell, spanning from the DNA sequence to protein expression. While single-cell transcriptomics is illuminating the cellular and molecular diversity across species and tissues, the 3D spatial context of these molecular data is often overlooked. Here, I discuss emerging 3D tissue histology techniques that add the missing third spatial dimension to biomedical research. Through innovations in tissue-clearing chemistry, labeling and volumetric imaging that enhance 3D reconstructions and their synergy with molecular techniques, these technologies will provide detailed blueprints of entire organs or organisms at the cellular level. Machine learning, especially deep learning, will be essential for extracting meaningful insights from the vast data. Further development of integrated structural, molecular and computational methods will unlock the full potential of next-generation 3D histology.
Collapse
Affiliation(s)
- Ali Ertürk
- Institute for Tissue Engineering and Regenerative Medicine, Helmholtz Zentrum München, Neuherberg, Germany.
- Institute for Stroke and Dementia Research, Klinikum der Universität München, Ludwig-Maximilians University, Munich, Germany.
- School of Medicine, Koç University, İstanbul, Turkey.
- Deep Piction GmbH, Munich, Germany.
| |
Collapse
|
18
|
Khanal S, Liu Y, Bamidele AO, Wixom AQ, Washington AM, Jalan-Sakrikar N, Cooper SA, Vuckovic I, Zhang S, Zhong J, Johnson KL, Charlesworth MC, Kim I, Yeon Y, Yoon S, Noh YK, Meroueh C, Timbilla AA, Yaqoob U, Gao J, Kim Y, Lucien F, Huebert RC, Hay N, Simons M, Shah VH, Kostallari E. Glycolysis in hepatic stellate cells coordinates fibrogenic extracellular vesicle release spatially to amplify liver fibrosis. SCIENCE ADVANCES 2024; 10:eadn5228. [PMID: 38941469 PMCID: PMC11212729 DOI: 10.1126/sciadv.adn5228] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/13/2023] [Accepted: 05/24/2024] [Indexed: 06/30/2024]
Abstract
Liver fibrosis is characterized by the activation of perivascular hepatic stellate cells (HSCs), the release of fibrogenic nanosized extracellular vesicles (EVs), and increased HSC glycolysis. Nevertheless, how glycolysis in HSCs coordinates fibrosis amplification through tissue zone-specific pathways remains elusive. Here, we demonstrate that HSC-specific genetic inhibition of glycolysis reduced liver fibrosis. Moreover, spatial transcriptomics revealed a fibrosis-mediated up-regulation of EV-related pathways in the liver pericentral zone, which was abrogated by glycolysis genetic inhibition. Mechanistically, glycolysis in HSCs up-regulated the expression of EV-related genes such as Ras-related protein Rab-31 (RAB31) by enhancing histone 3 lysine 9 acetylation on the promoter region, which increased EV release. Functionally, these glycolysis-dependent EVs increased fibrotic gene expression in recipient HSC. Furthermore, EVs derived from glycolysis-deficient mice abrogated liver fibrosis amplification in contrast to glycolysis-competent mouse EVs. In summary, glycolysis in HSCs amplifies liver fibrosis by promoting fibrogenic EV release in the hepatic pericentral zone, which represents a potential therapeutic target.
Collapse
Affiliation(s)
- Shalil Khanal
- Division of Gastroenterology and Hepatology, Mayo Clinic, Rochester, MN 55905, USA
| | - Yuanhang Liu
- Department of Quantitative Health Sciences, Mayo Clinic, Rochester, MN 55905, USA
| | | | - Alexander Q. Wixom
- Division of Gastroenterology and Hepatology, Mayo Clinic, Rochester, MN 55905, USA
- Department of Quantitative Health Sciences, Mayo Clinic, Rochester, MN 55905, USA
| | - Alexander M. Washington
- Biochemistry and Molecular Biology Graduate Program, Mayo Clinic Graduate School of Biomedical Sciences, Mayo Clinic, Rochester, MN 55905, USA
| | - Nidhi Jalan-Sakrikar
- Division of Gastroenterology and Hepatology, Mayo Clinic, Rochester, MN 55905, USA
| | - Shawna A. Cooper
- Biochemistry and Molecular Biology Graduate Program, Mayo Clinic Graduate School of Biomedical Sciences, Mayo Clinic, Rochester, MN 55905, USA
| | - Ivan Vuckovic
- Metabolomics Core, Mayo Clinic, Rochester, MN 55905, USA
| | - Song Zhang
- Department of Cardiovascular Medicine, Mayo Clinic, Rochester, MN 55905, USA
| | - Jun Zhong
- Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, MN 55905, USA
| | | | | | - Iljung Kim
- Department of Computer Science, Hanyang University, Seoul 04763, Republic of South Korea
| | - Yubin Yeon
- Department of Computer Science, Hanyang University, Seoul 04763, Republic of South Korea
| | - Sangwoong Yoon
- School of Computational Sciences, Korea Institute for Advanced Study, Seoul 02455, Republic of South Korea
| | - Yung-Kyun Noh
- Department of Computer Science, Hanyang University, Seoul 04763, Republic of South Korea
- School of Computational Sciences, Korea Institute for Advanced Study, Seoul 02455, Republic of South Korea
| | - Chady Meroueh
- Department of Pathology, Division of Anatomic Pathology, Mayo Clinic, Rochester, MN 55905, USA
| | - Abdul Aziz Timbilla
- Division of Gastroenterology and Hepatology, Mayo Clinic, Rochester, MN 55905, USA
- Department of Medical Biochemistry, Faculty of Medicine, Hradec Kralove, Charles University, Hradec Kralove, Czech Republic
| | - Usman Yaqoob
- Division of Gastroenterology and Hepatology, Mayo Clinic, Rochester, MN 55905, USA
| | - Jinhang Gao
- Division of Gastroenterology and Hepatology, Mayo Clinic, Rochester, MN 55905, USA
- Lab of Gastroenterology and Hepatology, State Key Laboratory of Biotherapy; Department of Gastroenterology, West China Hospital, Sichuan University, Chengdu, China
| | - Yohan Kim
- Department of Urology, Mayo Clinic, Rochester, MN 55905, USA
| | - Fabrice Lucien
- Department of Urology, Mayo Clinic, Rochester, MN 55905, USA
| | - Robert C. Huebert
- Division of Gastroenterology and Hepatology, Mayo Clinic, Rochester, MN 55905, USA
| | - Nissim Hay
- Department of Biochemistry and Molecular Genetics, College of Medicine, University of Illinois at Chicago, Chicago, IL 60607, USA
| | - Michael Simons
- Cardiovascular Research Center, Yale University, New Haven, CI 06510, USA
| | - Vijay H. Shah
- Division of Gastroenterology and Hepatology, Mayo Clinic, Rochester, MN 55905, USA
| | - Enis Kostallari
- Division of Gastroenterology and Hepatology, Mayo Clinic, Rochester, MN 55905, USA
| |
Collapse
|
19
|
Roberts DS, Loo JA, Tsybin YO, Liu X, Wu S, Chamot-Rooke J, Agar JN, Paša-Tolić L, Smith LM, Ge Y. Top-down proteomics. NATURE REVIEWS. METHODS PRIMERS 2024; 4:38. [PMID: 39006170 PMCID: PMC11242913 DOI: 10.1038/s43586-024-00318-2] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Accepted: 04/24/2024] [Indexed: 07/16/2024]
Abstract
Proteoforms, which arise from post-translational modifications, genetic polymorphisms and RNA splice variants, play a pivotal role as drivers in biology. Understanding proteoforms is essential to unravel the intricacies of biological systems and bridge the gap between genotypes and phenotypes. By analysing whole proteins without digestion, top-down proteomics (TDP) provides a holistic view of the proteome and can decipher protein function, uncover disease mechanisms and advance precision medicine. This Primer explores TDP, including the underlying principles, recent advances and an outlook on the future. The experimental section discusses instrumentation, sample preparation, intact protein separation, tandem mass spectrometry techniques and data collection. The results section looks at how to decipher raw data, visualize intact protein spectra and unravel data analysis. Additionally, proteoform identification, characterization and quantification are summarized, alongside approaches for statistical analysis. Various applications are described, including the human proteoform project and biomedical, biopharmaceutical and clinical sciences. These are complemented by discussions on measurement reproducibility, limitations and a forward-looking perspective that outlines areas where the field can advance, including potential future applications.
Collapse
Affiliation(s)
- David S Roberts
- Department of Chemistry, Stanford University, Stanford, CA, USA
- Sarafan ChEM-H, Stanford University, Stanford, CA, USA
| | - Joseph A Loo
- Department of Chemistry and Biochemistry, Department of Biological Chemistry, University of California - Los Angeles, Los Angeles, CA, USA
| | | | - Xiaowen Liu
- Deming Department of Medicine, School of Medicine, Tulane University, New Orleans, LA, USA
| | - Si Wu
- Department of Chemistry and Biochemistry, The University of Alabama, Tuscaloosa, AL, USA
| | | | - Jeffrey N Agar
- Departments of Chemistry and Chemical Biology and Pharmaceutical Sciences, Northeastern University, Boston, MA, USA
| | - Ljiljana Paša-Tolić
- Environmental and Molecular Sciences Division, Pacific Northwest National Laboratory, Richland, WA, USA
| | - Lloyd M Smith
- Department of Chemistry, University of Wisconsin, Madison, WI, USA
| | - Ying Ge
- Department of Chemistry, University of Wisconsin, Madison, WI, USA
- Department of Cell and Regenerative Biology, Human Proteomics Program, University of Wisconsin - Madison, Madison, WI, USA
| |
Collapse
|
20
|
Pang M, Jones JJ, Wang TY, Quan B, Kubat NJ, Qiu Y, Roukes ML, Chou TF. Increasing Proteome Coverage Through a Reduction in Analyte Complexity in Single-Cell Equivalent Samples. J Proteome Res 2024. [PMID: 38832920 DOI: 10.1021/acs.jproteome.4c00062] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/06/2024]
Abstract
The advancement of sophisticated instrumentation in mass spectrometry has catalyzed an in-depth exploration of complex proteomes. This exploration necessitates a nuanced balance in experimental design, particularly between quantitative precision and the enumeration of analytes detected. In bottom-up proteomics, a key challenge is that oversampling of abundant proteins can adversely affect the identification of a diverse array of unique proteins. This issue is especially pronounced in samples with limited analytes, such as small tissue biopsies or single-cell samples. Methods such as depletion and fractionation are suboptimal to reduce oversampling in single cell samples, and other improvements on LC and mass spectrometry technologies and methods have been developed to address the trade-off between precision and enumeration. We demonstrate that by using a monosubstrate protease for proteomic analysis of single-cell equivalent digest samples, an improvement in quantitative accuracy can be achieved, while maintaining high proteome coverage established by trypsin. This improvement is particularly vital for the field of single-cell proteomics, where single-cell samples with limited number of protein copies, especially in the context of low-abundance proteins, can benefit from considering analyte complexity. Considerations about analyte complexity, alongside chromatographic complexity, integration with data acquisition methods, and other factors such as those involving enzyme kinetics, will be crucial in the design of future single-cell workflows.
Collapse
Affiliation(s)
- Marion Pang
- Division of Biology and Biological Engineering, California Institute of Technology, 1200 East California Boulevard, Pasadena, California 91125, United States
| | - Jeff J Jones
- Division of Biology and Biological Engineering, California Institute of Technology, 1200 East California Boulevard, Pasadena, California 91125, United States
- Proteome Exploration Laboratory, Beckman Institute, California Institute of Technology, 1200 East California Boulevard, Pasadena, California 91125, United States
| | - Ting-Yu Wang
- Division of Biology and Biological Engineering, California Institute of Technology, 1200 East California Boulevard, Pasadena, California 91125, United States
- Proteome Exploration Laboratory, Beckman Institute, California Institute of Technology, 1200 East California Boulevard, Pasadena, California 91125, United States
| | - Baiyi Quan
- Division of Physics, Mathematics and Astronomy, California Institute of Technology, 1200 East California Boulevard, Pasadena, California 91125, United States
| | - Nicole J Kubat
- Division of Physics, Mathematics and Astronomy, California Institute of Technology, 1200 East California Boulevard, Pasadena, California 91125, United States
| | - Yanping Qiu
- Division of Biology and Biological Engineering, California Institute of Technology, 1200 East California Boulevard, Pasadena, California 91125, United States
- Proteome Exploration Laboratory, Beckman Institute, California Institute of Technology, 1200 East California Boulevard, Pasadena, California 91125, United States
| | - Michael L Roukes
- Division of Biology and Biological Engineering, California Institute of Technology, 1200 East California Boulevard, Pasadena, California 91125, United States
- Division of Physics, Mathematics and Astronomy, California Institute of Technology, 1200 East California Boulevard, Pasadena, California 91125, United States
- Division of Engineering and Applied Science, California Institute of Technology, 1200 East California Blvd, Pasadena, California 91125, United States
| | - Tsui-Fen Chou
- Division of Biology and Biological Engineering, California Institute of Technology, 1200 East California Boulevard, Pasadena, California 91125, United States
- Proteome Exploration Laboratory, Beckman Institute, California Institute of Technology, 1200 East California Boulevard, Pasadena, California 91125, United States
| |
Collapse
|
21
|
Munro V, Kelly V, Messner CB, Kustatscher G. Cellular control of protein levels: A systems biology perspective. Proteomics 2024; 24:e2200220. [PMID: 38012370 DOI: 10.1002/pmic.202200220] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/23/2023] [Revised: 11/13/2023] [Accepted: 11/15/2023] [Indexed: 11/29/2023]
Abstract
How cells regulate protein levels is a central question of biology. Over the past decades, molecular biology research has provided profound insights into the mechanisms and the molecular machinery governing each step of the gene expression process, from transcription to protein degradation. Recent advances in transcriptomics and proteomics have complemented our understanding of these fundamental cellular processes with a quantitative, systems-level perspective. Multi-omic studies revealed significant quantitative, kinetic and functional differences between the genome, transcriptome and proteome. While protein levels often correlate with mRNA levels, quantitative investigations have demonstrated a substantial impact of translation and protein degradation on protein expression control. In addition, protein-level regulation appears to play a crucial role in buffering protein abundances against undesirable mRNA expression variation. These findings have practical implications for many fields, including gene function prediction and precision medicine.
Collapse
Affiliation(s)
- Victoria Munro
- Wellcome Centre for Cell Biology, University of Edinburgh, Edinburgh, UK
| | - Van Kelly
- Wellcome Centre for Cell Biology, University of Edinburgh, Edinburgh, UK
| | - Christoph B Messner
- Precision Proteomics Center, Swiss Institute of Allergy and Asthma Research (SIAF), University of Zurich, Davos, Switzerland
| | - Georg Kustatscher
- Wellcome Centre for Cell Biology, University of Edinburgh, Edinburgh, UK
| |
Collapse
|
22
|
Yang Z, Jin K, Chen Y, Liu Q, Chen H, Hu S, Wang Y, Pan Z, Feng F, Shi M, Xie H, Ma H, Zhou H. AM-DMF-SCP: Integrated Single-Cell Proteomics Analysis on an Active Matrix Digital Microfluidic Chip. JACS AU 2024; 4:1811-1823. [PMID: 38818059 PMCID: PMC11134390 DOI: 10.1021/jacsau.4c00027] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/08/2024] [Revised: 03/08/2024] [Accepted: 03/08/2024] [Indexed: 06/01/2024]
Abstract
Single-cell proteomics offers unparalleled insights into cellular diversity and molecular mechanisms, enabling a deeper understanding of complex biological processes at the individual cell level. Here, we develop an integrated sample processing on an active-matrix digital microfluidic chip for single-cell proteomics (AM-DMF-SCP). Employing the AM-DMF-SCP approach and data-independent acquisition (DIA), we identify an average of 2258 protein groups in single HeLa cells within 15 min of the liquid chromatography gradient. We performed comparative analyses of three tumor cell lines: HeLa, A549, and HepG2, and machine learning was utilized to identify the unique features of these cell lines. Applying the AM-DMF-SCP to characterize the proteomes of a third-generation EGFR inhibitor, ASK120067-resistant cells (67R) and their parental NCI-H1975 cells, we observed a potential correlation between elevated VIM expression and 67R resistance, which is consistent with the findings from bulk sample analyses. These results suggest that AM-DMF-SCP is an automated, robust, and sensitive platform for single-cell proteomics and demonstrate the potential for providing valuable insights into cellular mechanisms.
Collapse
Affiliation(s)
- Zhicheng Yang
- Department
of Analytical Chemistry, State Key Laboratory of Drug Research, Shanghai
Institute of Materia Medica, Chinese Academy
of Sciences, Shanghai 201203, China
- University
of the Chinese Academy of Sciences, Beijing 100049, China
| | - Kai Jin
- CAS
Key Laboratory of Bio-Medical Diagnostics, Suzhou Institute of Biomedical
Engineering and Technology, Chinese Academy
of Sciences, Suzhou 215163, China
| | - Yimin Chen
- Department
of Analytical Chemistry, State Key Laboratory of Drug Research, Shanghai
Institute of Materia Medica, Chinese Academy
of Sciences, Shanghai 201203, China
- University
of the Chinese Academy of Sciences, Beijing 100049, China
| | - Qian Liu
- Department
of Analytical Chemistry, State Key Laboratory of Drug Research, Shanghai
Institute of Materia Medica, Chinese Academy
of Sciences, Shanghai 201203, China
| | - Hongxu Chen
- School
of Chinese Materia Medica, Nanjing University
of Chinese Medicine, 138 Xianlin Avenue, Nanjing, Jiangsu 210023, China
| | - Siyi Hu
- CAS
Key Laboratory of Bio-Medical Diagnostics, Suzhou Institute of Biomedical
Engineering and Technology, Chinese Academy
of Sciences, Suzhou 215163, China
| | - Yuqiu Wang
- Department
of Analytical Chemistry, State Key Laboratory of Drug Research, Shanghai
Institute of Materia Medica, Chinese Academy
of Sciences, Shanghai 201203, China
| | - Zilu Pan
- Division
of Antitumor Pharmacology, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai 201203, China
| | - Fang Feng
- Division
of Antitumor Pharmacology, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai 201203, China
| | - Mude Shi
- Guangdong
ACXEL Micro & Nano Tech Co. Ltd., Foshan, Guangdong Province 528000, China
| | - Hua Xie
- University
of the Chinese Academy of Sciences, Beijing 100049, China
- Zhongshan
Institute for Drug Discovery, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Zhongshan 528400, China
- Division
of Antitumor Pharmacology, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai 201203, China
| | - Hanbin Ma
- CAS
Key Laboratory of Bio-Medical Diagnostics, Suzhou Institute of Biomedical
Engineering and Technology, Chinese Academy
of Sciences, Suzhou 215163, China
- Guangdong
ACXEL Micro & Nano Tech Co. Ltd., Foshan, Guangdong Province 528000, China
| | - Hu Zhou
- Department
of Analytical Chemistry, State Key Laboratory of Drug Research, Shanghai
Institute of Materia Medica, Chinese Academy
of Sciences, Shanghai 201203, China
- University
of the Chinese Academy of Sciences, Beijing 100049, China
- Hangzhou
Institute for Advanced Study, University
of Chinese Academy of Sciences, Hangzhou 310024, China
| |
Collapse
|
23
|
Makhmut A, Qin D, Hartlmayr D, Seth A, Coscia F. An Automated and Fast Sample Preparation Workflow for Laser Microdissection Guided Ultrasensitive Proteomics. Mol Cell Proteomics 2024; 23:100750. [PMID: 38513891 PMCID: PMC11067455 DOI: 10.1016/j.mcpro.2024.100750] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/08/2023] [Revised: 03/13/2024] [Accepted: 03/18/2024] [Indexed: 03/23/2024] Open
Abstract
Spatial tissue proteomics integrating whole-slide imaging, laser microdissection, and ultrasensitive mass spectrometry is a powerful approach to link cellular phenotypes to functional proteome states in (patho)physiology. To be applicable to large patient cohorts and low sample input amounts, including single-cell applications, loss-minimized and streamlined end-to-end workflows are key. We here introduce an automated sample preparation protocol for laser microdissected samples utilizing the cellenONE robotic system, which has the capacity to process 192 samples in 3 h. Following laser microdissection collection directly into the proteoCHIP LF 48 or EVO 96 chip, our optimized protocol facilitates lysis, formalin de-crosslinking, and tryptic digest of low-input archival tissue samples. The seamless integration with the Evosep ONE LC system by centrifugation allows 'on-the-fly' sample clean-up, particularly pertinent for laser microdissection workflows. We validate our method in human tonsil archival tissue, where we profile proteomes of spatially-defined B-cell, T-cell, and epithelial microregions of 4000 μm2 to a depth of ∼2000 proteins and with high cell type specificity. We finally provide detailed equipment templates and experimental guidelines for broad accessibility.
Collapse
Affiliation(s)
- Anuar Makhmut
- Max-Delbrück-Center for Molecular Medicine in the Helmholtz Association (MDC), Spatial Proteomics Group, Berlin, Germany
| | - Di Qin
- Max-Delbrück-Center for Molecular Medicine in the Helmholtz Association (MDC), Spatial Proteomics Group, Berlin, Germany
| | | | | | - Fabian Coscia
- Max-Delbrück-Center for Molecular Medicine in the Helmholtz Association (MDC), Spatial Proteomics Group, Berlin, Germany.
| |
Collapse
|
24
|
Coorssen JR, Padula MP. Proteomics-The State of the Field: The Definition and Analysis of Proteomes Should Be Based in Reality, Not Convenience. Proteomes 2024; 12:14. [PMID: 38651373 PMCID: PMC11036260 DOI: 10.3390/proteomes12020014] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/17/2024] [Revised: 04/17/2024] [Accepted: 04/17/2024] [Indexed: 04/25/2024] Open
Abstract
With growing recognition and acknowledgement of the genuine complexity of proteomes, we are finally entering the post-proteogenomic era. Routine assessment of proteomes as inferred correlates of gene sequences (i.e., canonical 'proteins') cannot provide the necessary critical analysis of systems-level biology that is needed to understand underlying molecular mechanisms and pathways or identify the most selective biomarkers and therapeutic targets. These critical requirements demand the analysis of proteomes at the level of proteoforms/protein species, the actual active molecular players. Currently, only highly refined integrated or integrative top-down proteomics (iTDP) enables the analytical depth necessary to provide routine, comprehensive, and quantitative proteome assessments across the widest range of proteoforms inherent to native systems. Here we provide a broad perspective of the field, taking in historical and current realities, to establish a more balanced understanding of where the field has come from (in particular during the ten years since Proteomes was launched), current issues, and how things likely need to proceed if necessary deep proteome analyses are to succeed. We base this in our firm belief that the best proteomic analyses reflect, as closely as possible, the native sample at the moment of sampling. We also seek to emphasise that this and future analytical approaches are likely best based on the broad recognition and exploitation of the complementarity of currently successful approaches. This also emphasises the need to continuously evaluate and further optimize established approaches, to avoid complacency in thinking and expectations but also to promote the critical and careful development and introduction of new approaches, most notably those that address proteoforms. Above all, we wish to emphasise that a rigorous focus on analytical quality must override current thinking that largely values analytical speed; the latter would certainly be nice, if only proteoforms could thus be effectively, routinely, and quantitatively assessed. Alas, proteomes are composed of proteoforms, not molecular species that can be amplified or that directly mirror genes (i.e., 'canonical'). The problem is hard, and we must accept and address it as such, but the payoff in playing this longer game of rigorous deep proteome analyses is the promise of far more selective biomarkers, drug targets, and truly personalised or even individualised medicine.
Collapse
Affiliation(s)
- Jens R. Coorssen
- Department of Biological Sciences, Faculty of Mathematics and Science, Brock University, St. Catharines, ON L2S 3A1, Canada
- Institute for Globally Distributed Open Research and Education (IGDORE), St. Catharines, ON L2N 4X2, Canada
| | - Matthew P. Padula
- School of Life Sciences and Proteomics, Lipidomics and Metabolomics Core Facility, Faculty of Science, University of Technology Sydney, Sydney, NSW 2007, Australia
| |
Collapse
|
25
|
Nalehua MR, Zaia J. A critical evaluation of ultrasensitive single-cell proteomics strategies. Anal Bioanal Chem 2024; 416:2359-2369. [PMID: 38358530 DOI: 10.1007/s00216-024-05171-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/25/2023] [Revised: 01/20/2024] [Accepted: 01/23/2024] [Indexed: 02/16/2024]
Abstract
Success of mass spectrometry characterization of the proteome of single cells allows us to gain a greater understanding than afforded by transcriptomics alone but requires clear understanding of the tradeoffs between analytical throughput and precision. Recent advances in mass spectrometry acquisition techniques, including updated instrumentation and sample preparation, have improved the quality of peptide signals obtained from single cell data. However, much of the proteome remains uncharacterized, and higher throughput techniques often come at the expense of reduced sensitivity and coverage, which diminish the ability to measure proteoform heterogeneity, including splice variants and post-translational modifications, in single cell data analysis. Here, we assess the growing body of ultrasensitive single-cell approaches and their tradeoffs as researchers try to balance throughput and precision in their experiments.
Collapse
Affiliation(s)
| | - Joseph Zaia
- Bioinformatics Program, Boston University, Boston, MA, USA.
- Department of Biochemistry and Cell Biology, Boston University, Boston, MA, USA.
| |
Collapse
|
26
|
Truong T, Kelly RT. What's new in single-cell proteomics. Curr Opin Biotechnol 2024; 86:103077. [PMID: 38359605 PMCID: PMC11068367 DOI: 10.1016/j.copbio.2024.103077] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/27/2023] [Accepted: 01/19/2024] [Indexed: 02/17/2024]
Abstract
In recent years, single-cell proteomics (SCP) has advanced significantly, enabling the analysis of thousands of proteins within single mammalian cells. This progress is driven by advances in experimental design, with maturing label-free and multiplexed methods, optimized sample preparation, and innovations in separation techniques, including ultra-low-flow nanoLC. These factors collectively contribute to improved sensitivity, throughput, and reproducibility. Cutting-edge mass spectrometry platforms and data acquisition approaches continue to play a critical role in enhancing data quality. Furthermore, the exploration of spatial proteomics with single-cell resolution offers significant promise for understanding cellular interactions, giving rise to various phenotypes. SCP has far-reaching applications in cancer research, biomarker discovery, and developmental biology. Here, we provide a critical review of recent advances in the field of SCP.
Collapse
Affiliation(s)
- Thy Truong
- Department of Chemistry and Biochemistry, Brigham Young University, Provo, UT 84602, United States
| | - Ryan T Kelly
- Department of Chemistry and Biochemistry, Brigham Young University, Provo, UT 84602, United States.
| |
Collapse
|
27
|
Zhao CL, Mou HZ, Pan JB, Xing L, Mo Y, Kang B, Chen HY, Xu JJ. AI-assisted mass spectrometry imaging with in situ image segmentation for subcellular metabolomics analysis. Chem Sci 2024; 15:4547-4555. [PMID: 38516065 PMCID: PMC10952063 DOI: 10.1039/d4sc00839a] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/05/2024] [Accepted: 02/20/2024] [Indexed: 03/23/2024] Open
Abstract
Subcellular metabolomics analysis is crucial for understanding intracellular heterogeneity and accurate drug-cell interactions. Unfortunately, the ultra-small size and complex microenvironment inside the cell pose a great challenge to achieving this goal. To address this challenge, we propose an artificial intelligence-assisted subcellular mass spectrometry imaging (AI-SMSI) strategy with in situ image segmentation. Based on the nanometer-resolution MSI technique, the protonated guanine and threonine ions were respectively employed as the nucleus and cytoplasmic markers to complete image segmentation at the subcellular level, avoiding mutual interference of signals from various compartments in the cell. With advanced AI models, the metabolites within the different regions could be further integrated and profiled. Through this method, we decrypted the distinct action mechanism of isomeric drugs, doxorubicin (DOX) and epirubicin (EPI), only with a stereochemical inversion at C-4'. Within the cytoplasmic region, fifteen specific metabolites were discovered as biomarkers for distinguishing the drug action difference between DOX and EPI. Moreover, we identified that the downregulations of glutamate and aspartate in the malate-aspartate shuttle pathway may contribute to the higher paratoxicity of DOX. Our current AI-SMSI approach has promising applications for subcellular metabolomics analysis and thus opens new opportunities to further explore drug-cell specific interactions for the long-term pursuit of precision medicine.
Collapse
Affiliation(s)
- Cong-Lin Zhao
- State Key Laboratory of Analytical Chemistry for Life Science, School of Chemistry and Chemical Engineering, Nanjing University Nanjing 210023 China
| | - Han-Zhang Mou
- State Key Laboratory of Analytical Chemistry for Life Science, School of Chemistry and Chemical Engineering, Nanjing University Nanjing 210023 China
| | - Jian-Bin Pan
- State Key Laboratory of Analytical Chemistry for Life Science, School of Chemistry and Chemical Engineering, Nanjing University Nanjing 210023 China
| | - Lei Xing
- State Key Laboratory of Analytical Chemistry for Life Science, School of Chemistry and Chemical Engineering, Nanjing University Nanjing 210023 China
| | - Yuxiang Mo
- State Key Laboratory of Low-Dimensional Quantum Physics, Department of Physics, Tsinghua University Beijing 100084 China
| | - Bin Kang
- State Key Laboratory of Analytical Chemistry for Life Science, School of Chemistry and Chemical Engineering, Nanjing University Nanjing 210023 China
| | - Hong-Yuan Chen
- State Key Laboratory of Analytical Chemistry for Life Science, School of Chemistry and Chemical Engineering, Nanjing University Nanjing 210023 China
| | - Jing-Juan Xu
- State Key Laboratory of Analytical Chemistry for Life Science, School of Chemistry and Chemical Engineering, Nanjing University Nanjing 210023 China
| |
Collapse
|
28
|
Kang SWS, Cunningham RP, Miller CB, Brown LA, Cultraro CM, Harned A, Narayan K, Hernandez J, Jenkins LM, Lobanov A, Cam M, Porat-Shliom N. A spatial map of hepatic mitochondria uncovers functional heterogeneity shaped by nutrient-sensing signaling. Nat Commun 2024; 15:1799. [PMID: 38418824 PMCID: PMC10902380 DOI: 10.1038/s41467-024-45751-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/24/2023] [Accepted: 02/04/2024] [Indexed: 03/02/2024] Open
Abstract
In the liver, mitochondria are exposed to different concentrations of nutrients due to their spatial positioning across the periportal and pericentral axis. How the mitochondria sense and integrate these signals to respond and maintain homeostasis is not known. Here, we combine intravital microscopy, spatial proteomics, and functional assessment to investigate mitochondrial heterogeneity in the context of liver zonation. We find that periportal and pericentral mitochondria are morphologically and functionally distinct; beta-oxidation is elevated in periportal regions, while lipid synthesis is predominant in the pericentral mitochondria. In addition, comparative phosphoproteomics reveals spatially distinct patterns of mitochondrial composition and potential regulation via phosphorylation. Acute pharmacological modulation of nutrient sensing through AMPK and mTOR shifts mitochondrial phenotypes in the periportal and pericentral regions, linking nutrient gradients across the lobule and mitochondrial heterogeneity. This study highlights the role of protein phosphorylation in mitochondrial structure, function, and overall homeostasis in hepatic metabolic zonation. These findings have important implications for liver physiology and disease.
Collapse
Affiliation(s)
- Sun Woo Sophie Kang
- Cell Biology and Imaging Section, Thoracic and GI Malignancies Branch, National Cancer Institute (NCI), National Institutes of Health (NIH), Bethesda, MD, USA
| | - Rory P Cunningham
- Cell Biology and Imaging Section, Thoracic and GI Malignancies Branch, National Cancer Institute (NCI), National Institutes of Health (NIH), Bethesda, MD, USA
| | - Colin B Miller
- Cell Biology and Imaging Section, Thoracic and GI Malignancies Branch, National Cancer Institute (NCI), National Institutes of Health (NIH), Bethesda, MD, USA
| | - Lauryn A Brown
- Cell Biology and Imaging Section, Thoracic and GI Malignancies Branch, National Cancer Institute (NCI), National Institutes of Health (NIH), Bethesda, MD, USA
| | - Constance M Cultraro
- Cell Biology and Imaging Section, Thoracic and GI Malignancies Branch, National Cancer Institute (NCI), National Institutes of Health (NIH), Bethesda, MD, USA
| | - Adam Harned
- Center for Molecular Microscopy, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
- Cancer Research Technology Programs, Frederick National Laboratory for Cancer Research, Frederick, MD, USA
| | - Kedar Narayan
- Center for Molecular Microscopy, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
- Cancer Research Technology Programs, Frederick National Laboratory for Cancer Research, Frederick, MD, USA
| | - Jonathan Hernandez
- Surgical Oncology Program, National Cancer Institute (NCI), National Institutes of Health (NIH), Bethesda, MD, USA
| | - Lisa M Jenkins
- Laboratory of Cell Biology, National Cancer Institute (NCI), National Institutes of Health (NIH), Bethesda, MD, USA
| | - Alexei Lobanov
- CCR Collaborative Bioinformatics Resource (CCBR) National Cancer Institute (NCI), National Institutes of Health (NIH), Bethesda, MD, USA
| | - Maggie Cam
- CCR Collaborative Bioinformatics Resource (CCBR) National Cancer Institute (NCI), National Institutes of Health (NIH), Bethesda, MD, USA
| | - Natalie Porat-Shliom
- Cell Biology and Imaging Section, Thoracic and GI Malignancies Branch, National Cancer Institute (NCI), National Institutes of Health (NIH), Bethesda, MD, USA.
| |
Collapse
|
29
|
Shen B, Pade LR, Nemes P. The 15-min (Sub)Cellular Proteome. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.02.15.580399. [PMID: 38405838 PMCID: PMC10888744 DOI: 10.1101/2024.02.15.580399] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/27/2024]
Abstract
Single-cell mass spectrometry (MS) opens a proteomic window onto the inner workings of cells. Here, we report the discovery characterization of the subcellular proteome of single, identified embryonic cells in record speed and molecular coverage. We integrated subcellular capillary microsampling, fast capillary electrophoresis (CE), high-efficiency nano-flow electrospray ionization, and orbitrap tandem MS. In proof-of-principle tests, we found shorter separation times to hinder proteome detection using DDA, but not DIA. Within a 15-min effective separation window, CE data-independent acquisition (DIA) was able to identify 1,161 proteins from single HeLa-cell-equivalent (∼200 pg) proteome digests vs. 401 proteins by the reference data-dependent acquisition (DDA) on the same platform. The approach measured 1,242 proteins from subcellular niches in an identified cell in the live Xenopus laevis (frog) embryo, including many canonical components of organelles. CE-MS with DIA enables fast, sensitive, and deep profiling of the (sub)cellular proteome, expanding the bioanalytical toolbox of cell biology. Authorship Contributions P.N. and B.S. designed the study. L.R.P. collected the X. laevis cell aspirates. B.S. prepared and measured the samples. B.S. and P.N. analyzed the data and interpreted the results. P.N. and B.S. wrote the manuscript. All the authors commented on the manuscript.
Collapse
|
30
|
Porat-Shliom N. Compartmentalization, cooperation, and communication: The 3Cs of Hepatocyte zonation. Curr Opin Cell Biol 2024; 86:102292. [PMID: 38064779 PMCID: PMC10922296 DOI: 10.1016/j.ceb.2023.102292] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/18/2023] [Revised: 11/13/2023] [Accepted: 11/14/2023] [Indexed: 02/15/2024]
Abstract
The unique architecture of the liver allows for spatial compartmentalization of its functions, also known as liver zonation. In contrast to organelles and cells, this compartment is devoid of a surrounding membrane, rendering traditional biochemical tools ineffective for studying liver zonation. Recent advancements in tissue imaging and single-cell technologies have provided new insights into the complexity of tissue organization, rich cellular composition, and the gradients that shape zonation. Hepatocyte gene expression profiles and metabolic programs differ based on their location. Non-parenchymal cells further support hepatocytes from different zones through local secretion of factors that instruct hepatocyte activities. Collectively, these elements form a cohesive and dynamic network of cell-cell interactions that vary across space, time, and disease states. This review will examine the cell biology of hepatocytes in vivo, presenting the latest discoveries and emerging principles that govern tissue-level and sub-cellular compartmentalization.
Collapse
Affiliation(s)
- Natalie Porat-Shliom
- Cell Biology and Imaging Section, Thoracic and GI Malignancies Branch, Center for Cancer Research, National Cancer Institute, NIH, Bethesda, MD, USA.
| |
Collapse
|
31
|
Ctortecka C, Clark NM, Boyle B, Seth A, Mani DR, Udeshi ND, Carr SA. Automated single-cell proteomics providing sufficient proteome depth to study complex biology beyond cell type classifications. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.01.20.576369. [PMID: 38328197 PMCID: PMC10849471 DOI: 10.1101/2024.01.20.576369] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/09/2024]
Abstract
Mass spectrometry (MS)-based single-cell proteomics (SCP) has gained massive attention as a viable complement to other single cell approaches. The rapid technological and computational advances in the field have pushed the boundaries of sensitivity and throughput. However, reproducible quantification of thousands of proteins within a single cell at reasonable proteome depth to characterize biological phenomena remains a challenge. To address some of those limitations we present a combination of fully automated single cell sample preparation utilizing a dedicated chip within the picolitre dispensing robot, the cellenONE. The proteoCHIP EVO 96 can be directly interfaced with the Evosep One chromatographic system for in-line desalting and highly reproducible separation with a throughput of 80 samples per day. This, in combination with the Bruker timsTOF MS instruments, demonstrates double the identifications without manual sample handling. Moreover, relative to standard high-performance liquid chromatography, the Evosep One separation provides further 2-fold improvement in protein identifications. The implementation of the newest generation timsTOF Ultra with our proteoCHIP EVO 96-based sample preparation workflow reproducibly identifies up to 4,000 proteins per single HEK-293T without a carrier or match-between runs. Our current SCP depth spans over 4 orders of magnitude and identifies over 50 biologically relevant ubiquitin ligases. We complement our highly reproducible single-cell proteomics workflow to profile hundreds of lipopolysaccharide (LPS)-perturbed THP-1 cells and identified key regulatory proteins involved in interleukin and interferon signaling. This study demonstrates that the proteoCHIP EVO 96-based SCP sample preparation with the timsTOF Ultra provides sufficient proteome depth to study complex biology beyond cell-type classifications.
Collapse
Affiliation(s)
- Claudia Ctortecka
- Broad Institute of MIT and Harvard, 415 Main Street, 02142 Cambridge, MA, USA
| | - Natalie M. Clark
- Broad Institute of MIT and Harvard, 415 Main Street, 02142 Cambridge, MA, USA
| | - Brian Boyle
- Broad Institute of MIT and Harvard, 415 Main Street, 02142 Cambridge, MA, USA
| | - Anjali Seth
- Cellenion SASU, 60F avenue Rockefeller, 69008 Lyon, France
| | - D. R. Mani
- Broad Institute of MIT and Harvard, 415 Main Street, 02142 Cambridge, MA, USA
| | - Namrata D. Udeshi
- Broad Institute of MIT and Harvard, 415 Main Street, 02142 Cambridge, MA, USA
| | - Steven A. Carr
- Broad Institute of MIT and Harvard, 415 Main Street, 02142 Cambridge, MA, USA
| |
Collapse
|
32
|
Dowling P, Trollet C, Negroni E, Swandulla D, Ohlendieck K. How Can Proteomics Help to Elucidate the Pathophysiological Crosstalk in Muscular Dystrophy and Associated Multi-System Dysfunction? Proteomes 2024; 12:4. [PMID: 38250815 PMCID: PMC10801633 DOI: 10.3390/proteomes12010004] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/05/2023] [Revised: 01/09/2024] [Accepted: 01/12/2024] [Indexed: 01/23/2024] Open
Abstract
This perspective article is concerned with the question of how proteomics, which is a core technique of systems biology that is deeply embedded in the multi-omics field of modern bioresearch, can help us better understand the molecular pathogenesis of complex diseases. As an illustrative example of a monogenetic disorder that primarily affects the neuromuscular system but is characterized by a plethora of multi-system pathophysiological alterations, the muscle-wasting disease Duchenne muscular dystrophy was examined. Recent achievements in the field of dystrophinopathy research are described with special reference to the proteome-wide complexity of neuromuscular changes and body-wide alterations/adaptations. Based on a description of the current applications of top-down versus bottom-up proteomic approaches and their technical challenges, future systems biological approaches are outlined. The envisaged holistic and integromic bioanalysis would encompass the integration of diverse omics-type studies including inter- and intra-proteomics as the core disciplines for systematic protein evaluations, with sophisticated biomolecular analyses, including physiology, molecular biology, biochemistry and histochemistry. Integrated proteomic findings promise to be instrumental in improving our detailed knowledge of pathogenic mechanisms and multi-system dysfunction, widening the available biomarker signature of dystrophinopathy for improved diagnostic/prognostic procedures, and advancing the identification of novel therapeutic targets to treat Duchenne muscular dystrophy.
Collapse
Affiliation(s)
- Paul Dowling
- Department of Biology, Maynooth University, National University of Ireland, W23 F2H6 Maynooth, Co. Kildare, Ireland;
- Kathleen Lonsdale Institute for Human Health Research, Maynooth University, W23 F2H6 Maynooth, Co. Kildare, Ireland
| | - Capucine Trollet
- Center for Research in Myology U974, Sorbonne Université, INSERM, Myology Institute, 75013 Paris, France; (C.T.); (E.N.)
| | - Elisa Negroni
- Center for Research in Myology U974, Sorbonne Université, INSERM, Myology Institute, 75013 Paris, France; (C.T.); (E.N.)
| | - Dieter Swandulla
- Institute of Physiology, Faculty of Medicine, University of Bonn, D53115 Bonn, Germany;
| | - Kay Ohlendieck
- Department of Biology, Maynooth University, National University of Ireland, W23 F2H6 Maynooth, Co. Kildare, Ireland;
- Kathleen Lonsdale Institute for Human Health Research, Maynooth University, W23 F2H6 Maynooth, Co. Kildare, Ireland
| |
Collapse
|
33
|
Thielert M, Weiss CAM, Mann M, Rosenberger FA. Spatial Proteomics of Single Hepatocytes with Multiplexed Data-Independent Acquisition (mDIA). Methods Mol Biol 2024; 2817:97-113. [PMID: 38907150 DOI: 10.1007/978-1-0716-3934-4_9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/23/2024]
Abstract
Spatially resolved mass spectrometry-based proteomics at single-cell resolution promises to provide insights into biological heterogeneity. We describe a protocol based on multiplexed data-independent acquisition (mDIA) with dimethyl labeling to enhance proteome depth, accuracy, and throughput while minimizing costs. It enables high-quality proteome analysis of single isolated hepatocytes and utilizes liver zonation for single-cell proteomics benchmarking. This adaptable, modular protocol will promote the use of single-cell proteomics in spatial biology.
Collapse
Affiliation(s)
- Marvin Thielert
- Proteomics and Signal Transduction, Max Planck Institute of Biochemistry, Martinsried, Germany
| | - Caroline A M Weiss
- Proteomics and Signal Transduction, Max Planck Institute of Biochemistry, Martinsried, Germany
| | - Matthias Mann
- Proteomics and Signal Transduction, Max Planck Institute of Biochemistry, Martinsried, Germany
- Proteomics Program, Novo Nordisk Foundation Centre for Protein Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Florian A Rosenberger
- Proteomics and Signal Transduction, Max Planck Institute of Biochemistry, Martinsried, Germany.
| |
Collapse
|
34
|
Makhmut A, Qin D, Fritzsche S, Nimo J, König J, Coscia F. A framework for ultra-low-input spatial tissue proteomics. Cell Syst 2023; 14:1002-1014.e5. [PMID: 37909047 DOI: 10.1016/j.cels.2023.10.003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2023] [Revised: 08/03/2023] [Accepted: 10/06/2023] [Indexed: 11/02/2023]
Abstract
Spatial proteomics combining microscopy-based cell phenotyping with ultrasensitive mass-spectrometry-based proteomics is an emerging and powerful concept to study cell function and heterogeneity in (patho)physiology. However, optimized workflows that preserve morphological information for phenotype discovery and maximize proteome coverage of few or even single cells from laser microdissected tissue are currently lacking. Here, we report a robust and scalable workflow for the proteomic analysis of ultra-low-input archival material. Benchmarking in murine liver resulted in up to 2,000 quantified proteins from single hepatocyte contours and nearly 5,000 proteins from 50-cell regions. Applied to human tonsil, we profiled 146 microregions including T and B lymphocyte niches and quantified cell-type-specific markers, cytokines, and transcription factors. These data also highlighted proteome dynamics within activated germinal centers, illuminating sites undergoing B cell proliferation and somatic hypermutation. This approach has broad implications in biomedicine, including early disease profiling and drug target and biomarker discovery. A record of this paper's transparent peer review process is included in the supplemental information.
Collapse
Affiliation(s)
- Anuar Makhmut
- Max-Delbrück-Center for Molecular Medicine in the Helmholtz Association (MDC), Spatial Proteomics Group, Berlin, Germany
| | - Di Qin
- Max-Delbrück-Center for Molecular Medicine in the Helmholtz Association (MDC), Spatial Proteomics Group, Berlin, Germany
| | - Sonja Fritzsche
- Max-Delbrück-Center for Molecular Medicine in the Helmholtz Association (MDC), Spatial Proteomics Group, Berlin, Germany
| | - Jose Nimo
- Max-Delbrück-Center for Molecular Medicine in the Helmholtz Association (MDC), Spatial Proteomics Group, Berlin, Germany
| | - Janett König
- Max-Delbrück-Center for Molecular Medicine in the Helmholtz Association (MDC), Spatial Proteomics Group, Berlin, Germany
| | - Fabian Coscia
- Max-Delbrück-Center for Molecular Medicine in the Helmholtz Association (MDC), Spatial Proteomics Group, Berlin, Germany.
| |
Collapse
|