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1

Vistain, Luke F., and Savaş Tay. "Single-Cell Proteomics." Trends in Biochemical Sciences 46, no. 8 (2021): 661–72. http://dx.doi.org/10.1016/j.tibs.2021.01.013.

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Doerr, Allison. "Single-cell proteomics." Nature Methods 16, no. 1 (2018): 20. http://dx.doi.org/10.1038/s41592-018-0273-y.

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3

Suruchi, Sharma*1 And Sahil Sharma2. "Single Cell Proteomics (SCP): The Cell Analysis." Science World a monthly e magazine 3, no. 3 (2023): 413–17. https://doi.org/10.5281/zenodo.7762339.

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Cells from microbial cultures and mammalian cell cultures that have identical genomes and grow in the same environment do not have the same proteome. The distinctions Proteomes have important functional consequences. The proteome of each individual cell will be analysed using high throughput analytical platforms in Single Cell Proteomics; advanced isolation and sampling methods are required to minimize protein loss, and highly sensitive techniques are required for proteome analysis. Single Cell Proteomics can be used to create a proteome map of each type of cell in multicellular and single-cell organisms, interactions related to a biological process. This aids in the investigation of protein expression and modification under specific biological conditions, the characterization of protein functions in a genome, the identification of protein localization and compartmentalization at a given time, and the determination of protein-protein interactions related to a biological process.
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4

Senavirathna, Lakmini, Cheng Ma, Ru Chen, and Sheng Pan. "Spectral Library-Based Single-Cell Proteomics Resolves Cellular Heterogeneity." Cells 11, no. 15 (2022): 2450. http://dx.doi.org/10.3390/cells11152450.

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Dissecting the proteome of cell types and states at single-cell resolution, while being highly challenging, has significant implications in basic science and biomedicine. Mass spectrometry (MS)-based single-cell proteomics represents an emerging technology for system-wide, unbiased profiling of proteins in single cells. However, significant challenges remain in analyzing an extremely small amount of proteins collected from a single cell, as a proteome-wide amplification of proteins is not currently feasible. Here, we report an integrated spectral library-based single-cell proteomics (SLB-SCP) platform that is ultrasensitive and well suited for a large-scale analysis. To overcome the low MS/MS signal intensity intrinsically associated with a single-cell analysis, this approach takes an alternative approach by extracting a breadth of information that specifically defines the physicochemical characteristics of a peptide from MS1 spectra, including monoisotopic mass, isotopic distribution, and retention time (hydrophobicity), and uses a spectral library for proteomic identification. This conceptually unique MS platform, coupled with the DIRECT sample preparation method, enabled identification of more than 2000 proteins in a single cell to distinguish different proteome landscapes associated with cellular types and heterogeneity. We characterized individual normal and cancerous pancreatic ductal cells (HPDE and PANC-1, respectively) and demonstrated the substantial difference in the proteomes between HPDE and PANC-1 at the single-cell level. A significant upregulation of multiple protein networks in cancer hallmarks was identified in the PANC-1 cells, functionally discriminating the PANC-1 cells from the HPDE cells. This integrated platform can be built on high-resolution MS and widely accepted proteomic software, making it possible for community-wide applications.
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Chen, Kangfu, and Zongjie Wang. "A Micropillar Array Based Microfluidic Device for Rare Cell Detection and Single-Cell Proteomics." Methods and Protocols 6, no. 5 (2023): 80. http://dx.doi.org/10.3390/mps6050080.

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Advancements in single-cell-related technologies have opened new possibilities for analyzing rare cells, such as circulating tumor cells (CTCs) and rare immune cells. Among these techniques, single-cell proteomics, particularly single-cell mass spectrometric analysis (scMS), has gained significant attention due to its ability to directly measure transcripts without the need for specific reagents. However, the success of single-cell proteomics relies heavily on efficient sample preparation, as protein loss in low-concentration samples can profoundly impact the analysis. To address this challenge, an effective handling system for rare cells is essential for single-cell proteomic analysis. Herein, we propose a microfluidics-based method that offers highly efficient isolation, detection, and collection of rare cells (e.g., CTCs). The detailed fabrication process of the micropillar array-based microfluidic device is presented, along with its application for CTC isolation, identification, and collection for subsequent proteomic analysis.
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Krieg, Rene C., Cloud P. Paweletz, Lance A. Liotta, and Emanuel F. Petricoin. "Clinical Proteomics for Cancer Biomarker Discovery and Therapeutic Targeting." Technology in Cancer Research & Treatment 1, no. 4 (2002): 263–72. http://dx.doi.org/10.1177/153303460200100407.

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As we emerge into the post-genome era, proteomics finds itself as the driving force field as we translate the nucleic acid information archive into understanding how the cell actually works and how disease processes operate. Even so, the traditionally held view of proteomics as simply cataloging and developing lists of the cellular protein repertoire of a cell are now changing, especially in the sub-discipline of clinical proteomics. The most relevant information archive to clinical applications and drug development involves the elucidation of the information flow of the cell; the “software” of protein pathway networks and circuitry. The deranged circuitry of the cell as the drug target itself as well as the effect of the drug on not just the target, but also the entire network, is what we now are striving towards. Clinical proteomics, as a new and most exciting sub-discipline of proteomics, involves the bench-to-bedside clinical application of proteomic tools. Unlike the genome, there are potentially thousands of proteomes: each cell type has its own unique proteome. Moreover, each cell type can alter its proteome depending on the unique tissue microenvironment in which it resides, giving rise to multiple permutations of a single proteome. Since there is no polymerase chain reaction equivalent to proteomics- identifying and discovering the “wiring diagram” of a human diseased cell in a biopsy specimen remains a daunting challenge. New micro-proteomic technologies are being and still need to be developed to drill down into the proteomes of clinically relevant material. Cancer, as a model disease, provides a fertile environment to study the application of proteomics at the bedside. The promise of clinical proteomics and the new technologies that are developed is that we will detect cancer earlier through discovery of biomarkers, we will discover the next generation of targets and imaging biomarkers, and we can then apply this knowledge to patient-tailored therapy.
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7

Orsburn, Benjamin C. "Evaluation of the Sensitivity of Proteomics Methods Using the Absolute Copy Number of Proteins in a Single Cell as a Metric." Proteomes 9, no. 3 (2021): 34. http://dx.doi.org/10.3390/proteomes9030034.

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Proteomic technology has improved at a staggering pace in recent years, with even practitioners challenged to keep up with new methods and hardware. The most common metric used for method performance is the number of peptides and proteins identified. While this metric may be helpful for proteomics researchers shopping for new hardware, this is often not the most biologically relevant metric. Biologists often utilize proteomics in the search for protein regulators that are of a lower relative copy number in the cell. In this review, I re-evaluate untargeted proteomics data using a simple graphical representation of the absolute copy number of proteins present in a single cancer cell as a metric. By comparing single-shot proteomics data to the coverage of the most in-depth proteomic analysis of that cell line acquired to date, we can obtain a rapid metric of method performance. Using a simple copy number metric allows visualization of how proteomics has developed in both sensitivity and overall dynamic range when using both relatively long and short acquisition times. To enable reanalysis beyond what is presented here, two available web applications have been developed for single- and multi-experiment comparisons with reference protein copy number data for multiple cell lines and organisms.
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8

Slavov, Nikolai. "Scaling Up Single-Cell Proteomics." Molecular & Cellular Proteomics 21, no. 1 (2022): 100179. http://dx.doi.org/10.1016/j.mcpro.2021.100179.

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9

Ctortecka, Claudia, and Karl Mechtler. "The rise of single‐cell proteomics." Analytical Science Advances 2, no. 3-4 (2021): 84–94. http://dx.doi.org/10.1002/ansa.202000152.

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10

Petelski, Aleksandra A., Edward Emmott, Andrew Leduc, et al. "Multiplexed single-cell proteomics using SCoPE2." Nature Protocols 16, no. 12 (2021): 5398–425. http://dx.doi.org/10.1038/s41596-021-00616-z.

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11

Masuda, Takeshi. "Trends in Single-Cell Proteomics Technology." Journal of the Mass Spectrometry Society of Japan 70, no. 1 (2022): 72–73. http://dx.doi.org/10.5702/massspec.s22-13.

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12

Perkel, Jeffrey M. "Single-cell proteomics takes centre stage." Nature 597, no. 7877 (2021): 580–82. http://dx.doi.org/10.1038/d41586-021-02530-6.

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13

Kelly, Ryan T. "Single-cell Proteomics: Progress and Prospects." Molecular & Cellular Proteomics 19, no. 11 (2020): 1739–48. http://dx.doi.org/10.1074/mcp.r120.002234.

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MS-based proteome profiling has become increasingly comprehensive and quantitative, yet a persistent shortcoming has been the relatively large samples required to achieve an in-depth measurement. Such bulk samples, typically comprising thousands of cells or more, provide a population average and obscure important cellular heterogeneity. Single-cell proteomics capabilities have the potential to transform biomedical research and enable understanding of biological systems with a new level of granularity. Recent advances in sample processing, separations and MS instrumentation now make it possible to quantify >1000 proteins from individual mammalian cells, a level of coverage that required an input of thousands of cells just a few years ago. This review discusses important factors and parameters that should be optimized across the workflow for single-cell and other low-input measurements. It also highlights recent developments that have advanced the field and opportunities for further development.
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Marx, Vivien. "A dream of single-cell proteomics." Nature Methods 16, no. 9 (2019): 809–12. http://dx.doi.org/10.1038/s41592-019-0540-6.

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15

Diks, Sander H., and Maikel P. Peppelenbosch. "Single cell proteomics for personalised medicine." Trends in Molecular Medicine 10, no. 12 (2004): 574–77. http://dx.doi.org/10.1016/j.molmed.2004.10.005.

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16

Specht, Harrison, and Nikolai Slavov. "Transformative Opportunities for Single-Cell Proteomics." Journal of Proteome Research 17, no. 8 (2018): 2565–71. http://dx.doi.org/10.1021/acs.jproteome.8b00257.

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17

Rosenberger, Florian A., Marvin Thielert, and Matthias Mann. "Making single-cell proteomics biologically relevant." Nature Methods 20, no. 3 (2023): 320–23. http://dx.doi.org/10.1038/s41592-023-01771-9.

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18

Sipe, Sarah N., and Nikolai Slavov. "Single-Cell Proteomics Accelerates toward Proteoforms." Journal of Proteome Research 23, no. 5 (2024): 1545–46. http://dx.doi.org/10.1021/acs.jproteome.4c00290.

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19

Truong, Thy, and Ryan T. Kelly. "What’s new in single-cell proteomics." Current Opinion in Biotechnology 86 (April 2024): 103077. http://dx.doi.org/10.1016/j.copbio.2024.103077.

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20

Marx, Vivien. "Proteomics sets up single-cell and single-molecule solutions." Nature Methods 20, no. 3 (2023): 350–54. http://dx.doi.org/10.1038/s41592-023-01781-7.

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21

Arias-Hidalgo, Carlota, Pablo Juanes-Velasco, Alicia Landeira-Viñuela, et al. "Single-Cell Proteomics: The Critical Role of Nanotechnology." International Journal of Molecular Sciences 23, no. 12 (2022): 6707. http://dx.doi.org/10.3390/ijms23126707.

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In single-cell analysis, biological variability can be attributed to individual cells, their specific state, and the ability to respond to external stimuli, which are determined by protein abundance and their relative alterations. Mass spectrometry (MS)-based proteomics (e.g., SCoPE-MS and SCoPE2) can be used as a non-targeted method to detect molecules across hundreds of individual cells. To achieve high-throughput investigation, novel approaches in Single-Cell Proteomics (SCP) are needed to identify and quantify proteins as accurately as possible. Controlling sample preparation prior to LC-MS analysis is critical, as it influences sensitivity, robustness, and reproducibility. Several nanotechnological approaches have been developed for the removal of cellular debris, salts, and detergents, and to facilitate systematic sample processing at the nano- and microfluidic scale. In addition, nanotechnology has enabled high-throughput proteomics analysis, which have required the improvement of software tools, such as DART-ID or DO-MS, which are also fundamental for addressing key biological questions. Single-cell proteomics has many applications in nanomedicine and biomedical research, including advanced cancer immunotherapies or biomarker characterization, among others; and novel methods allow the quantification of more than a thousand proteins while analyzing hundreds of single cells.
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22

Slavov, Nikolai. "Counting protein molecules for single-cell proteomics." Cell 185, no. 2 (2022): 232–34. http://dx.doi.org/10.1016/j.cell.2021.12.013.

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23

Slavov, Nikolai. "Driving Single Cell Proteomics Forward with Innovation." Journal of Proteome Research 20, no. 11 (2021): 4915–18. http://dx.doi.org/10.1021/acs.jproteome.1c00639.

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24

Marusina, Kate. "Single-Cell Proteomics Is in the Chips." Genetic Engineering & Biotechnology News 35, no. 13 (2015): 1, 12, 14–15. http://dx.doi.org/10.1089/gen.35.13.01.

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25

Karlsson, Filip. "Single-Cell Spatial Proteomics by Molecular Pixelation." Genetic Engineering & Biotechnology News 43, no. 9 (2023): 56–58. http://dx.doi.org/10.1089/gen.43.09.18.

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26

Sarkar, Anjali. "Single-Cell Proteomics Bypasses Bottlenecks, Sways Skeptics." Genetic Engineering & Biotechnology News 43, no. 3 (2023): 52–55. http://dx.doi.org/10.1089/gen.43.03.15.

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27

Geiger, Tamar. "Tackling tumor complexity with single-cell proteomics." Nature Methods 20, no. 3 (2023): 324–26. http://dx.doi.org/10.1038/s41592-023-01784-4.

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28

Yang, Jennifer, Jing Zhou, and Sean Mackay. "Abstract LB094: First of its kind T cell receptors and functional proteomics detected from the same single cells to advance cancer immunology discovery." Cancer Research 82, no. 12_Supplement (2022): LB094. http://dx.doi.org/10.1158/1538-7445.am2022-lb094.

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Abstract T cell receptor repertoire diversity and T cell cytokine production are the important attributes for the assessment of effective T cell immunity against cancer and pathogens, yet existing single-cell genomics platforms cannot interrogate TCR sequences and proteomic cytokines simultaneously. IsoPlexis’ functional proteomics has demonstrated the values in cell therapy and cancer immunology against blood cancers and solid tumors. The ability to connect the T cell receptor diversity to the most potent single cells enables a wide variety of applications for tumor infiltrating lymphocytes, personalized neoantigen TCR’s, cancer immunology in terms of understanding antigen specificity, as well as self-potency. IsoPlexis single-cell TCR Duomic platform provide a unique measurement for connecting T cell receptor sequences to those T cell cytokine profiles, for the first time. Human PBMC-derived T cell line established from healthy donor was stimulated with PMA/ionomycin (2ul/ml, Biolegend) for 6hrs, stained and resuspended in Reverse transcription premix containing reverse transcriptase with buffer and protein phosphatase inhibitor at 1 x 106/ml and then loaded on Duomic chips. Ran on IsoLight overnight, cDNA was collected from the chip followed by cleanup and amplification, proceeded with TCR amplification and sequencing on NextSeq 1000/2000. Proteomic data from single cells was automatically generated and analyzed by IsoSpeak software. The single-cell TCR sequencing data on the IsoPlexis TCR Duomics platform shows that 78.9% cells have TCR alpha chain recombination and 94.4% cells have TCR beta chain recombination. Totally 77.7% cells have combination on both alpha and beta chain. VDJ Usage Plot further displays the top10 most common V (D) J combination flow between T cell receptor alpha chain and beta chain among the single cells, revealing the diversity of TCR repertoire at the single-cell level. In addition, the proteomics data identifies the dominant proteins among the single cells are Granzyme B, GM-CSF, IFN-g and TNF-a. Furthermore, we define 3 distinct cell clusters based on functional proteins and an association of VDJ distribution with these cell clusters, suggesting the occurrence of V(D)J sequences in the most common reconstructed alpha and beta sequence. IsoPlexis single-cell TCR Duomics platform, for the first time, enables simultaneous profiling of functional proteomics and TCR repertoires/TCR clonotypes as well as identification of specific TCR V(D)J combination with functional cell clusters across the same individual T cells. The combination of these two analytes at the single cells provides a key new modality of tracking the most potent and also the antigen specific immune cells together to fight cancer and infectious disease, for the first time. Citation Format: Jennifer Yang, Jing Zhou, Sean Mackay. First of its kind T cell receptors and functional proteomics detected from the same single cells to advance cancer immunology discovery [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr LB094.
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Tsai, Chia-Feng, Rui Zhao, Sarah M. Williams, et al. "An Improved Boosting to Amplify Signal with Isobaric Labeling (iBASIL) Strategy for Precise Quantitative Single-cell Proteomics." Molecular & Cellular Proteomics 19, no. 5 (2020): 828–38. http://dx.doi.org/10.1074/mcp.ra119.001857.

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Mass spectrometry (MS)-based proteomics has great potential for overcoming the limitations of antibody-based immunoassays for antibody-independent, comprehensive, and quantitative proteomic analysis of single cells. Indeed, recent advances in nanoscale sample preparation have enabled effective processing of single cells. In particular, the concept of using boosting/carrier channels in isobaric labeling to increase the sensitivity in MS detection has also been increasingly used for quantitative proteomic analysis of small-sized samples including single cells. However, the full potential of such boosting/carrier approaches has not been significantly explored, nor has the resulting quantitation quality been carefully evaluated. Herein, we have further evaluated and optimized our recent boosting to amplify signal with isobaric labeling (BASIL) approach, originally developed for quantifying phosphorylation in small number of cells, for highly effective analysis of proteins in single cells. This improved BASIL (iBASIL) approach enables reliable quantitative single-cell proteomics analysis with greater proteome coverage by carefully controlling the boosting-to-sample ratio (e.g. in general <100×) and optimizing MS automatic gain control (AGC) and ion injection time settings in MS/MS analysis (e.g. 5E5 and 300 ms, respectively, which is significantly higher than that used in typical bulk analysis). By coupling with a nanodroplet-based single cell preparation (nanoPOTS) platform, iBASIL enabled identification of ∼2500 proteins and precise quantification of ∼1500 proteins in the analysis of 104 FACS-isolated single cells, with the resulting protein profiles robustly clustering the cells from three different acute myeloid leukemia cell lines. This study highlights the importance of carefully evaluating and optimizing the boosting ratios and MS data acquisition conditions for achieving robust, comprehensive proteomic analysis of single cells.
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30

Yihunie, Fanuel Bizuayehu, Mequanint Addisu Belete, Gizachew Fentahun, Solomon Getachew, and Teshager Dubie. "Diagnostic and Therapeutic Application of Proteomics in Infectious Disease." Advances in Cell and Gene Therapy 2023 (August 24, 2023): 1–6. http://dx.doi.org/10.1155/2023/5510791.

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The study of an organism’s genome, often known as “genomics,” has advanced quickly, producing a wealth of publicly accessible genetic data. Despite how valuable the genome is; proteins essentially control most aspects of cell function. Proteomics, or the comprehensive study of proteins, has emerged as an important technology for disease characterization, diagnosis, prognosis, drug development, and therapy. Proteomics technologies are now used to support the diagnosis and treatment of both infectious and noninfectious diseases. Nevertheless, it is more difficult to describe a proteomic profile since a single gene product may result in a number of unique proteins, and proteins have a wider range of chemical configurations. The proteome profiles of a particular organism, tissue, or cell are impacted by a variety of environmental factors, including those triggered by infectious agents. This review intends to highlight the applications of proteomics in the study of disease diagnosis and treatment. In this review, the different technologies used in proteomics studies, like two-dimensional gel electrophoresis, mass spectrometry, and protein microarray as well as biomarker discovery and drug target identification using proteomics, have also been focused on.
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31

Vitorino, Rui, Sofia Guedes, João Pinto da Costa, and Václav Kašička. "Microfluidics for Peptidomics, Proteomics, and Cell Analysis." Nanomaterials 11, no. 5 (2021): 1118. http://dx.doi.org/10.3390/nano11051118.

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Microfluidics is the advanced microtechnology of fluid manipulation in channels with at least one dimension in the range of 1–100 microns. Microfluidic technology offers a growing number of tools for manipulating small volumes of fluid to control chemical, biological, and physical processes relevant to separation, analysis, and detection. Currently, microfluidic devices play an important role in many biological, chemical, physical, biotechnological and engineering applications. There are numerous ways to fabricate the necessary microchannels and integrate them into microfluidic platforms. In peptidomics and proteomics, microfluidics is often used in combination with mass spectrometric (MS) analysis. This review provides an overview of using microfluidic systems for peptidomics, proteomics and cell analysis. The application of microfluidics in combination with MS detection and other novel techniques to answer clinical questions is also discussed in the context of disease diagnosis and therapy. Recent developments and applications of capillary and microchip (electro)separation methods in proteomic and peptidomic analysis are summarized. The state of the art of microchip platforms for cell sorting and single-cell analysis is also discussed. Advances in detection methods are reported, and new applications in proteomics and peptidomics, quality control of peptide and protein pharmaceuticals, analysis of proteins and peptides in biomatrices and determination of their physicochemical parameters are highlighted.
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32

Willison, Keith R., and David R. Klug. "Quantitative single cell and single molecule proteomics for clinical studies." Current Opinion in Biotechnology 24, no. 4 (2013): 745–51. http://dx.doi.org/10.1016/j.copbio.2013.06.001.

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33

Bozorgui, Behnaz, Zeynep Dereli, Guillaume Thibault, John N. Weinstein, and Anil Korkut. "Abstract 3765: Single cell spatial proteomics analysis and computational evaluation pipeline." Cancer Research 84, no. 6_Supplement (2024): 3765. http://dx.doi.org/10.1158/1538-7445.am2024-3765.

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Abstract Resolving tissue and proteomic heterogeneity is critical to decoding the structure and function of tumor-immune microenvironment (TIME). Such understanding requires profiling of tumor and immune cell proteomic features with spatial resolution at the single-cell level. Although such spatially resolved methods and data sets are becoming increasingly available, analytical and computational methods that can extract the highly complex features and interactions within TIME are lacking. To address that problem, we have developed a computational pipeline we call the Spatial Proteomics Analysis and Computational Evaluation Pipeline (SPACE). The SPACE pipeline is composed of many analysis modules for processing and mining highly multiplexed imaging-based data types to explore TIME composition, organization, and heterogeneity. The pipeline generates and interprets biomarker expression and positional information from multiplexed images using algorithms for image indexing, image registration, quality control, segmentation, identification and removal of non-specific signals, data normalization, automatic identification of missing data, and adjustment for left-over signals. The accurate intensity measurements at single cell level are then used to calculate the proposed spatial features that represent cellular interactions in TIME. A hierarchical decision tree of cell markers is used to annotate types and identities for individual cells. The SPACE enables statistical and differential analyses of complex spatial features as well as cell types/identities with respect to clinical annotations and genomic alterations. The visualization of spatial and imaging data is made possible through an open-source OMERO image repository and spatial maps that integrate diverse markers in a single representation. Here, we demonstrate the applications of our pipeline in diverse gastrointestinal tumor types (e.g., small bowel adenocarcinoma) and validate the importance of integrating tissue heterogeneity at spatial and single-cell level. The framework is applicable to nearly all highly multiplexed imaging data platforms, including but not limited to, CycIF, CODEX, and imaging mass cytometry. Citation Format: Behnaz Bozorgui, Zeynep Dereli, Guillaume Thibault, John N. Weinstein, Anil Korkut. Single cell spatial proteomics analysis and computational evaluation pipeline [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 3765.
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34

Goto-Silva, Livia, and Magno Junqueira. "Single-cell proteomics: A treasure trove in neurobiology." Biochimica et Biophysica Acta (BBA) - Proteins and Proteomics 1869, no. 7 (2021): 140658. http://dx.doi.org/10.1016/j.bbapap.2021.140658.

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35

Beck, Lir, and Tamar Geiger. "MS-based technologies for untargeted single-cell proteomics." Current Opinion in Biotechnology 76 (August 2022): 102736. http://dx.doi.org/10.1016/j.copbio.2022.102736.

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36

Xu, Bo, Wei Du, Bi-Feng Liu, and Qingming Luo. "Single Cell Proteomics: Challenge for Current Analytical Science." Current Analytical Chemistry 2, no. 1 (2006): 67–76. http://dx.doi.org/10.2174/157341106775197402.

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37

Gavasso, Sonia, Stein-Erik Gullaksen, Jørn Skavland, and Bjørn T. Gjertsen. "Single-cell proteomics: potential implications for cancer diagnostics." Expert Review of Molecular Diagnostics 16, no. 5 (2016): 579–89. http://dx.doi.org/10.1586/14737159.2016.1156531.

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38

Mayer, Rupert L., and Karl Mechtler. "Immunopeptidomics in the Era of Single-Cell Proteomics." Biology 12, no. 12 (2023): 1514. http://dx.doi.org/10.3390/biology12121514.

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Immunopeptidomics, as the analysis of antigen peptides being presented to the immune system via major histocompatibility complexes (MHC), is being seen as an imperative tool for identifying epitopes for vaccine development to treat cancer and viral and bacterial infections as well as parasites. The field has made tremendous strides over the last 25 years but currently still faces challenges in sensitivity and throughput for widespread applications in personalized medicine and large vaccine development studies. Cutting-edge technological advancements in sample preparation, liquid chromatography as well as mass spectrometry, and data analysis, however, are currently transforming the field. This perspective showcases how the advent of single-cell proteomics has accelerated this transformation of immunopeptidomics in recent years and will pave the way for even more sensitive and higher-throughput immunopeptidomics analyses.
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39

Luo, Xuhao, Jui-Yi Chen, Marzieh Ataei, and Abraham Lee. "Microfluidic Compartmentalization Platforms for Single Cell Analysis." Biosensors 12, no. 2 (2022): 58. http://dx.doi.org/10.3390/bios12020058.

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Many cellular analytical technologies measure only the average response from a cell population with an assumption that a clonal population is homogenous. The ensemble measurement often masks the difference among individual cells that can lead to misinterpretation. The advent of microfluidic technology has revolutionized single-cell analysis through precise manipulation of liquid and compartmentalizing single cells in small volumes (pico- to nano-liter). Due to its advantages from miniaturization, microfluidic systems offer an array of capabilities to study genomics, transcriptomics, and proteomics of a large number of individual cells. In this regard, microfluidic systems have emerged as a powerful technology to uncover cellular heterogeneity and expand the depth and breadth of single-cell analysis. This review will focus on recent developments of three microfluidic compartmentalization platforms (microvalve, microwell, and microdroplets) that target single-cell analysis spanning from proteomics to genomics. We also compare and contrast these three microfluidic platforms and discuss their respective advantages and disadvantages in single-cell analysis.
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40

Loya, Matthew, Tine Casneuf, Helene Bon, et al. "Abstract 2990: Single-slide FFPE proteomic profiling enables therapeutic target quantification and molecular subtyping in non-Hodgkin’s lymphoma and bone marrow tumor biopsies." Cancer Research 85, no. 8_Supplement_1 (2025): 2990. https://doi.org/10.1158/1538-7445.am2025-2990.

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Abstract Molecular profiling of formalin-fixed paraffin-embedded (FFPE) tumors in clinical research is performed with low-plex immunohistochemistry or genome-wide transcriptomics. The application of unbiased, highly multiplexed proteomics to FFPE is limited by sample input requirements, assay throughput and complex bioinformatics. Here we report a proteomic method amenable to low-input FFPE profiling which complements transcriptomics. We applied Biognosys’ mass-spectrometry platform (TrueDiscovery, DIA-MS) to samples encountered in routine histopathology laboratories (e.g. B-cell malignancies [n = 35], and breast, pancreatic and colorectal tumors [n=8]). FFPE samples were deparafinzed, de-crosslinked followed by trypsin digestion, and resulted peptides were analyzed by directDIA. Impact of tissue staining by Hematoxylin and Eosin (H&E) and acid decalcification was also assessed. We compared a deep-proteome workflow on a Thermo Scientific Orbitrap Exploris 480 to a novel high throughput workflow on a Bruker timsTOF HT. The deep-proteome workflow with single-slide FFPE tissues (median area 35mm2) generated comprehensive proteome coverage: ∼12,000 proteins detected overall and ∼7,000 detected in the majority of samples. The high throughput Bruker workflow delivered similar data with 80% less input in 20% of the time. Proteomes from H&E stained, cover-slipped slides were nearly identical to adjacent unstained tissue. Interestingly, proteomics profiling was successful in acid decalcified leukemia bone marrow biopsies whereas RNA was not recoverable. To assess the detection of key tissue biomarkers for therapeutic decision-making, we compiled a list of FDA approved antibody based therapeutic targets and found 20 of 21 were detectable in our samples (e.g. PDL1, PD1, LAG3, CD19, CD20 and CD38). Similarly, >80% of 55 immuno-oncology targets undergoing clinical development were detected. A key effector function of antibody therapeutics is complement-dependent cytotoxicity. The presence of complement components was confirmed by proteomics, however, some were undetectable by transcriptomics. Diffuse Large B-cell Lymphoma samples in this study were molecularly subtyped for a key prognostic, Cell of Origin (COO), using a CLIA validated RNA assay (Lymph2Cx). Well studied proteins (e.g. MUM1 and CD10) showed expected distribution based on patient COO, demonstrating proteomics can recapitulate established subtyping methods. We demonstrate that proteomics profiling can be conducted on single FFPE slides even with pre-processed tissue. This high-throughput workflow enables real-time analysis of Phase I trials or large tissue banks. Furthermore, proteome profiling provides an opportunity to consolidate testing for multiple therapeutic targets instead of serial IHC assays, maximizing the use of precious tissue biopsies. Citation Format: Matthew Loya, Tine Casneuf, Helene Bon, Manling Ma-Edmonds, Angelo Harris, Daniel Martin, Anantharaman Muthuswamy, Dominique Kamber, Martin Mehnert, Amaury Lachaud, Yuehan Feng, Mark Fereshteh, Omar Jabado. Single-slide FFPE proteomic profiling enables therapeutic target quantification and molecular subtyping in non-Hodgkin’s lymphoma and bone marrow tumor biopsies [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2025; Part 1 (Regular Abstracts); 2025 Apr 25-30; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2025;85(8_Suppl_1):Abstract nr 2990.
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41

Rosenberger, Florian A., Marvin Thielert, Maximilian T. Strauss, et al. "Spatial single-cell mass spectrometry defines zonation of the hepatocyte proteome." Nature Methods 20, no. 10 (2023): 1530–36. http://dx.doi.org/10.1038/s41592-023-02007-6.

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AbstractSingle-cell proteomics by mass spectrometry is emerging as a powerful and unbiased method for the characterization of biological heterogeneity. So far, it has been limited to cultured cells, whereas an expansion of the method to complex tissues would greatly enhance biological insights. Here we describe single-cell Deep Visual Proteomics (scDVP), a technology that integrates high-content imaging, laser microdissection and multiplexed mass spectrometry. scDVP resolves the context-dependent, spatial proteome of murine hepatocytes at a current depth of 1,700 proteins from a cell slice. Half of the proteome was differentially regulated in a spatial manner, with protein levels changing dramatically in proximity to the central vein. We applied machine learning to proteome classes and images, which subsequently inferred the spatial proteome from imaging data alone. scDVP is applicable to healthy and diseased tissues and complements other spatial proteomics and spatial omics technologies.
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42

Irish, Jonathan M., Nikesh Kotecha, and Garry P. Nolan. "Mapping normal and cancer cell signalling networks: towards single-cell proteomics." Nature Reviews Cancer 6, no. 2 (2006): 146–55. http://dx.doi.org/10.1038/nrc1804.

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43

Ding, Shifan, Na Lu, and Hassan Abolhassani. "Assessing the Influence of Selected Permeabilization Methods on Lymphocyte Single-Cell Multi-Omics." Antibodies 14, no. 1 (2025): 15. https://doi.org/10.3390/antib14010015.

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(1) Background: Single-cell multi-omics is a powerful method for the dissection and detection of complicated immunologic functions and synapses. However, most currently available technologies merge datasets of different omics from separate portions of the same sample to generate combined multi-omics. This process is a source of bias, mainly in the field of immunology on cells originating from pluripotent hematopoietic stem cells with high flexibility during maturation. (2) Methods: Although new multi-omics approaches have been developed to use the advantages of cellular and molecular barcoding and next-generation sequencing to solve this issue, one of the main current challenges is intracellular proteomics, which should be combined with other omics data with high importance for immune system studies. We designed this study to evaluate previously recommended minimal permeabilization and fixation methods on the quality and quantity of transcriptomics and proteomics data generated by the BD Rhapsody™ Single-Cell Analysis System. (3) Results: Our findings showed that high-throughput sequencing with advanced quality and read-out is required for the combination of multi-omics outcomes from a permeabilized single cell. Therefore, the HiseqX platform was selected for further analysis. The effect of immune stimulation was observed clearly as the separated clusters of helper and cytotoxic T cells using unsupervised clustering. Importantly, fixation and permeabilization did not affect the general expression profile of unstimulated cells. However, fixation and permeabilization were proved to negatively impact the detection of the whole transcriptome for single-cell assay. Nevertheless, about 60% of the transcriptomic signature of the stimulation was detected. If the measurement of combined surface and intracellular markers is required to be achieved, the modified fixation and permeabilization method is recommended because of a lower transcriptomic loss and more precise proteomic fingerprint detected. (4) Conclusions: The findings of this study support the potential possibility for integrating intracellular proteomics, which needs to be optimized and tested with newly designed oligonucleotide-tagged antibodies targeting intracellular proteins.
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44

Lokhov, Petr G., Elena E. Balashova, Oxana P. Trifonova, Dmitry L. Maslov, and Alexander I. Archakov. "Cell Proteomic Footprinting: Advances in the Quality of Cellular and Cell-Derived Cancer Vaccines." Pharmaceutics 15, no. 2 (2023): 661. http://dx.doi.org/10.3390/pharmaceutics15020661.

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In omics sciences, many compounds are measured simultaneously in a sample in a single run. Such analytical performance opens up prospects for improving cellular cancer vaccines and other cell-based immunotherapeutics. This article provides an overview of proteomics technology, known as cell proteomic footprinting. The molecular phenotype of cells is highly variable, and their antigenic profile is affected by many factors, including cell isolation from the tissue, cell cultivation conditions, and storage procedures. This makes the therapeutic properties of cells, including those used in vaccines, unpredictable. Cell proteomic footprinting makes it possible to obtain controlled cell products. Namely, this technology facilitates the cell authentication and quality control of cells regarding their molecular phenotype, which is directly connected with the antigenic properties of cell products. Protocols for cell proteomic footprinting with their crucial moments, footprint processing, and recommendations for the implementation of this technology are described in this paper. The provided footprints in this paper and program source code for their processing contribute to the fast implementation of this technology in the development and manufacturing of cell-based immunotherapeutics.
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45

Liu, Hui-Lin, Aysha H. Osmani, Leena Ukil, et al. "Single-Step Affinity Purification for Fungal Proteomics." Eukaryotic Cell 9, no. 5 (2010): 831–33. http://dx.doi.org/10.1128/ec.00032-10.

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ABSTRACT A single-step protein affinity purification protocol using Aspergillus nidulans is described. Detailed protocols for cell breakage, affinity purification, and depending on the application, methods for protein release from affinity beads are provided. Examples defining the utility of the approaches, which should be widely applicable, are included.
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46

Jung, Yugyung, Minkook Son, Yu Ri Nam, Jongchan Choi, James R. Heath, and Sung Yang. "Microfluidic Single-Cell Proteomics Assay Chip: Lung Cancer Cell Line Case Study." Micromachines 12, no. 10 (2021): 1147. http://dx.doi.org/10.3390/mi12101147.

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Cancer is a dynamic disease involving constant changes. With these changes, cancer cells become heterogeneous, resulting in varying sensitivity to chemotherapy. The heterogeneity of cancer cells plays a key role in chemotherapy resistance and cancer recurrence. Therefore, for effective treatment, cancer cells need to be analyzed at the single-cell level by monitoring various proteins and investigating their heterogeneity. We propose a microfluidic chip for a single-cell proteomics assay that is capable of analyzing complex cellular signaling systems to reveal the heterogeneity of cancer cells. The single-cell assay chip comprises (i) microchambers (n = 1376) for manipulating single cancer cells, (ii) micropumps for rapid single-cell lysis, and (iii) barcode immunosensors for detecting nine different secretory and intracellular proteins to reveal the correlation among cancer-related proteins. Using this chip, the single-cell proteomics of a lung cancer cell line, which may be easily masked in bulk analysis, were evaluated. By comparing changes in the level of protein secretion and heterogeneity in response to combinations of four anti-cancer drugs, this study suggests a new method for selecting the best combination of anti-cancer drugs. Subsequent preclinical and clinical trials should enable this platform to become applicable for patient-customized therapies.
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47

Vu, Hung M., Ju Yeon Lee, Yongmin Kim, et al. "Exploring the feasibility of a single-protoplast proteomic analysis." Journal of Analytical Science and Technology 15, no. 1 (2024). http://dx.doi.org/10.1186/s40543-024-00457-x.

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Abstract Background Recent advances in high-resolution mass spectrometry have now enabled the study of proteomes at the single-cell level, offering the potential to unveil novel aspects of cellular processes. Remarkably, there has been no prior attempt to investigate single-plant cell proteomes. In this study, we aimed to explore the feasibility of conducting a proteomic analysis on individual protoplasts. Findings As a result, our analysis identified 978 proteins from the 180 protoplasts, aligning with well-known biological processes in plant leaves, such as photosynthetic electron transport in photosystem II. Employing the SCP package in the SCoPE2 workflow revealed a notable batch effect and extensive missing values in the data. Following correction, we observed the heterogeneity in single-protoplast proteome expression. Comparing the results of single-protoplast proteomics with those of bulk leaf proteomics, we noted that only a small fraction of bulk data was detected in the single-protoplast proteomics data, highlighting a technical limitation of the current single-cell proteomics method. Conclusions In summary, we demonstrated the feasibility of conducting a single-protoplast proteomic experiment, revealing heterogeneity in plant cellular proteome expression. This underscores the importance of analyzing a substantial number of plant cells to discern statistically significant changes in plant cell proteomes upon perturbation such as abscisic acid treatment in future studies. We anticipate that our study will contribute to advancing single-protoplast proteomics in the near future.
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48

Gebreyesus, Sofani Tafesse, Asad Ali Siyal, Reta Birhanu Kitata, et al. "Streamlined single-cell proteomics by an integrated microfluidic chip and data-independent acquisition mass spectrometry." Nature Communications 13, no. 1 (2022). http://dx.doi.org/10.1038/s41467-021-27778-4.

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AbstractSingle-cell proteomics can reveal cellular phenotypic heterogeneity and cell-specific functional networks underlying biological processes. Here, we present a streamlined workflow combining microfluidic chips for all-in-one proteomic sample preparation and data-independent acquisition (DIA) mass spectrometry (MS) for proteomic analysis down to the single-cell level. The proteomics chips enable multiplexed and automated cell isolation/counting/imaging and sample processing in a single device. Combining chip-based sample handling with DIA-MS using project-specific mass spectral libraries, we profile on average ~1,500 protein groups across 20 single mammalian cells. Applying the chip-DIA workflow to profile the proteomes of adherent and non-adherent malignant cells, we cover a dynamic range of 5 orders of magnitude with good reproducibility and <16% missing values between runs. Taken together, the chip-DIA workflow offers all-in-one cell characterization, analytical sensitivity and robustness, and the option to add additional functionalities in the future, thus providing a basis for advanced single-cell proteomics applications.
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Wang, Fang, Chunpu Liu, Jiawei Li, et al. "SPDB: a comprehensive resource and knowledgebase for proteomic data at the single-cell resolution." Nucleic Acids Research, November 11, 2023. http://dx.doi.org/10.1093/nar/gkad1018.

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Abstract The single-cell proteomics enables the direct quantification of protein abundance at the single-cell resolution, providing valuable insights into cellular phenotypes beyond what can be inferred from transcriptome analysis alone. However, insufficient large-scale integrated databases hinder researchers from accessing and exploring single-cell proteomics, impeding the advancement of this field. To fill this deficiency, we present a comprehensive database, namely Single-cell Proteomic DataBase (SPDB, https://scproteomicsdb.com/), for general single-cell proteomic data, including antibody-based or mass spectrometry-based single-cell proteomics. Equipped with standardized data process and a user-friendly web interface, SPDB provides unified data formats for convenient interaction with downstream analysis, and offers not only dataset-level but also protein-level data search and exploration capabilities. To enable detailed exhibition of single-cell proteomic data, SPDB also provides a module for visualizing data from the perspectives of cell metadata or protein features. The current version of SPDB encompasses 133 antibody-based single-cell proteomic datasets involving more than 300 million cells and over 800 marker/surface proteins, and 10 mass spectrometry-based single-cell proteomic datasets involving more than 4000 cells and over 7000 proteins. Overall, SPDB is envisioned to be explored as a useful resource that will facilitate the wider research communities by providing detailed insights into proteomics from the single-cell perspective.
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50

Mun, Dong-Gi, Firdous A. Bhat, Neha Joshi, et al. "Diversity of post-translational modifications and cell signaling revealed by single cell and single organelle mass spectrometry." Communications Biology 7, no. 1 (2024). http://dx.doi.org/10.1038/s42003-024-06579-7.

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AbstractThe rapid evolution of mass spectrometry-based single-cell proteomics now enables the cataloging of several thousand proteins from single cells. We investigated whether we could discover cellular heterogeneity beyond proteome, encompassing post-translational modifications (PTM), protein-protein interaction, and variants. By optimizing the mass spectrometry data interpretation strategy to enable the detection of PTMs and variants, we have generated a high-definition dataset of single-cell and nuclear proteomic-states. The data demonstrate the heterogeneity of cell-states and signaling dependencies at the single-cell level and reveal epigenetic drug-induced changes in single nuclei. This approach enables the exploration of previously uncharted single-cell and organellar proteomes revealing molecular characteristics that are inaccessible through RNA profiling.
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