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1. Native Proteomics by Capillary Zone Electrophoresis‐Mass Spectrometry

   https://doi.org/10.1002/anie.202408370  

   Wang, Qianyi; Wang, Qianjie; Qi, Zihao; Moeller, William; Wysocki, Vicki H; Sun, Liangliang (November 2024, Angewandte Chemie International Edition)

   Abstract Native proteomics measures endogenous proteoforms and protein complexes under a near physiological condition using native mass spectrometry (nMS) coupled with liquid‐phase separations. Native proteomics should provide the most accurate bird's‐eye view of proteome dynamics within cells, which is fundamental for understanding almost all biological processes. nMS has been widely employed to characterize well‐purified protein complexes. However, there are only very few trials of utilizing nMS to measure proteoforms and protein complexes in a complex sample (i.e., a whole cell lysate). Here, we pioneer the native proteomics measurement of large proteoforms or protein complexes up to 400 kDa from a complex proteome via online coupling of native capillary zone electrophoresis (nCZE) to an ultra‐high mass range (UHMR) Orbitrap mass spectrometer. The nCZE‐MS technique enabled the measurement of a 115‐kDa standard protein complex while consuming only about 0.1 ng of protein material. nCZE‐MS analysis of anE.colicell lysate detected 72 proteoforms or protein complexes in a mass range of 30–400 kDa in a single run while consuming only 50‐ng protein material. The mass distribution of detected proteoforms or protein complexes agreed well with that from mass photometry measurement. This work represents a technical breakthrough in native proteomics for measuring complex proteomes. 

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2. Recent advances (2019–2021) of capillary electrophoresis‐mass spectrometry for multilevel proteomics

   https://doi.org/10.1002/mas.21714  

   Chen, Daoyang; McCool, Elijah N.; Yang, Zhichang; Shen, Xiaojing; Lubeckyj, Rachele A.; Xu, Tian; Wang, Qianjie; Sun, Liangliang (June 2021, Mass Spectrometry Reviews)

   Multilevel proteomics aims to delineate proteins at the peptide (bottom-up proteomics), proteoform (top-down proteomics), and protein complex (native proteomics) levels. Capillary electrophoresis-mass spectrometry (CE-MS) can achieve highly efficient separation and highly sensitive detection of complex mixtures of peptides, proteoforms, and even protein complexes because of its substantial technical progress. CE-MS has become a valuable alternative to the routinely used liquid chromatography-mass spectrometry for multilevel proteomics. This review summarizes the most recent (2019-2021) advances of CE-MS for multilevel proteomics regarding technological progress and biological applications. We also provide brief perspectives on CE-MS for multilevel proteomics at the end, highlighting some future directions and potential challenges. 

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3. SCOT+: A Comprehensive Software Suite for Single-Cell alignment Using Optimal Transport

   https://doi.org/10.1093/bioadv/vbaf314  

   Baker, Colin; Pham, Tuan; Demetci, Pinar; Tran, Quang Huy; Redko, Ievgen; Sandstede, Bjorn; Singh, Ritambhara (December 2025, Bioinformatics Advances)

   Abstract SummaryNew advances in single-cell multi-omics experiments have allowed biologists to examine how various biological factors regulate processes in concert on the cellular level. However, measuring multiple cellular features for a single cell can be quite resource-intensive or impossible with the current technology. By using optimal transport (OT) to align cells and features across disparate datasets produced by separate assays, Single Cell alignment using Optimal Transport + (SCOT+), our unsupervised single-cell alignment software suite, allows biologists to align their data without the need for any correspondence. SCOT+ implements a generic optimal transport solution that can be reduced to multiple different previously studied OT optimization procedures including SCOT, SCOTv2, SCOOTR, and AGW for single cell, each of which provides state-of-the-art single-cell alignment performance. Outside of giving a unified framework to interact with prior formulations, the generality of SCOT+ optimization naturally gives rise to a new OT loss, Unbalanced Augmented Gromov-Wasserstein (UAGW), and a corresponding optimizer. With our user-friendly website and tutorials, this new package will help improve biological analyses by allowing for more accurate downstream analyses on multi-omics single-cell measurements. Implementation and AvailabilityOur algorithm is implemented in Pytorch and available on PyPI and GitHub (https://github.com/scotplus/scotplus). Additionally, we have many tutorials available in a separate GitHub repository (https://github.com/scotplus/book_source) and on our website (https://scotplus.github.io/). 

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4. modSaRa: a computationally efficient R package for CNV identification

   https://doi.org/10.1093/bioinformatics/btx212  

   Xiao, Feifei; Niu, Yue; Hao, Ning; Xu, Yanxun; Jin, Zhilin; Zhang, Heping (August 2017, Bioinformatics)

   Hancock, John (Ed.)

   Abstract SummaryChromosomal copy number variation (CNV) refers to a polymorphism that a DNA segment presents deletion or duplication in the population. The computational algorithms developed to identify this type of variation are usually of high computational complexity. Here we present a user-friendly R package, modSaRa, designed to perform copy number variants identification. The package is developed based on a change-point based method with optimal computational complexity and desirable accuracy. The current version of modSaRa package is a comprehensive tool with integration of preprocessing steps and main CNV calling steps. Availability and ImplementationmodSaRa is an R package written in R, C ++ and Rcpp and is now freely available for download at http://c2s2.yale.edu/software/modSaRa. Supplementary informationSupplementary data are available at Bioinformatics online. 

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5. Top-down proteomics

   https://doi.org/10.1038/s43586-024-00318-2  

   Roberts, David S; Loo, Joseph A; Tsybin, Yury O; Liu, Xiaowen; Wu, Si; Chamot-Rooke, Julia; Agar, Jeffrey N; Paša-Tolić, Ljiljana; Smith, Lloyd M; Ge, Ying (June 2024, Nature Reviews Methods Primers)

   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. 

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