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Journal articles on the topic 'Label free identification'

Author: Grafiati

Published: 29 March 2025

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1

Li, Jing, Hua Xu, Graham M. West, and Lyn H. Jones. "Label-free technologies for target identification and validation." MedChemComm 7, no. 5 (2016): 769–77. http://dx.doi.org/10.1039/c6md00045b.

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Chemical probes have been instrumental in revealing new targets and confirming target engagement. However, substantial effort and resources are required to design and synthesize these probes. In contrast, label-free technologies have the advantage of bypassing the need for chemical probes. Here we highlight the recent developments in label-free methods and discuss the pros and cons of each approach.

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2

Nickelsen, Anna, and Joachim Jose. "Label-free flow cytometry-based enzyme inhibitor identification." Analytica Chimica Acta 1179 (September 2021): 338826. http://dx.doi.org/10.1016/j.aca.2021.338826.

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Lloyd, William R., Shailesh Agarwal, Sagar U. Nigwekar, Karen Esmonde-White, Shawn Loder, Shawn Fagan, Jeremy Goverman, et al. "Raman spectroscopy for label-free identification of calciphylaxis." Journal of Biomedical Optics 20, no. 8 (August 11, 2015): 080501. http://dx.doi.org/10.1117/1.jbo.20.8.080501.

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4

Li, Huafeng, Qingsong Hu, and Zhanxuan Hu. "Catalyst for Clustering-Based Unsupervised Object Re-identification: Feature Calibration." Proceedings of the AAAI Conference on Artificial Intelligence 38, no. 4 (March 24, 2024): 3091–99. http://dx.doi.org/10.1609/aaai.v38i4.28092.

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Clustering-based methods are emerging as a ubiquitous technology in unsupervised object Re-Identification (ReID), which alternate between pseudo-label generation and representation learning. Recent advances in this field mainly fall into two groups: pseudo-label correction and robust representation learning. Differently, in this work, we improve unsupervised object ReID from feature calibration, a completely different but complementary insight from the current approaches. Specifically, we propose to insert a conceptually simple yet empirically powerful Feature Calibration Module (FCM) before pseudo-label generation. In practice, FCM calibrates the features using a nonparametric graph attention network, enforcing similar instances to move together in the feature space while allowing dissimilar instances to separate. As a result, we can generate more reliable pseudo-labels using the calibrated features and further improve subsequent representation learning. FCM is simple, effective, parameter-free, training-free, plug-and-play, and can be considered as a catalyst, increasing the ’chemical reaction’ between pseudo-label generation and representation learning. Moreover, it maintains the efficiency of testing time with negligible impact on training time. In this paper, we insert FCM into a simple baseline. Experiments across different scenarios and benchmarks show that FCM consistently improves the baseline (e.g., 8.2% mAP gain on MSMT17), and achieves the new state-of-the-art results. Code is available at: https://github.com/lhf12278/FCM-ReID.

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Choi, Junseo, Zheng Jia, Ramin Riahipour, Collin J. McKinney, Charuni A. Amarasekara, Kumuditha M. Weerakoon‐Ratnayake, Steven A. Soper, and Sunggook Park. "Label‐Free Identification of Single Mononucleotides by Nanoscale Electrophoresis." Small 17, no. 42 (September 23, 2021): 2102567. http://dx.doi.org/10.1002/smll.202102567.

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6

Choi, Junseo, Zheng Jia, Ramin Riahipour, Collin J. McKinney, Charuni A. Amarasekara, Kumuditha M. Weerakoon‐Ratnayake, Steven A. Soper, and Sunggook Park. "Label‐Free Identification of Single Mononucleotides by Nanoscale Electrophoresis." Small 17, no. 42 (September 23, 2021): 2102567. http://dx.doi.org/10.1002/smll.202102567.

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7

Dannhauser, David, Paolo Antonio Netti, and Filippo Causa. "Label-free scattering snapshot classification for living cell identification." EPJ Web of Conferences 309 (2024): 10021. http://dx.doi.org/10.1051/epjconf/202430910021.

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A scattering snapshot hold an enormous potential for cell class and state classification, allowing to avoid costly fluorescence labelling. Beside convolutional neural networks show outstanding image classification performance compared to other state-of-the-art methods, regarding accuracy and speed. Therefore, we combined the two techniques (Light Scattering and Deep Learning) to identify living cells with high precision. Neural Networks show high prediction performance for known classes but struggles when unknown classes need to be identified. In such a scenario no prior knowledge of the unknown cell class can be used for the model training, which inevitably results in a misclassification. To overcome the hurdle, of identifying unknown cell classes, we must first define an in-distribution of known snapshots to afterwards detect out of distribution snapshots as unknowns. Ones, such a new cell class is identified, we can retrain our cell classifier with the obtained knowledge, so we dynamically update the cell class database. We applied this measurement approach to scattering pattern snapshots of different classes of living cells. Our outcome shows a precise cell classification, which can be applied to a wide range of single cell classification approaches.

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Paidi, Santosh Kumar, Soumik Siddhanta, Robert Strouse, James B. McGivney, Christopher Larkin, and Ishan Barman. "Rapid Identification of Biotherapeutics with Label-Free Raman Spectroscopy." Analytical Chemistry 88, no. 8 (April 8, 2016): 4361–68. http://dx.doi.org/10.1021/acs.analchem.5b04794.

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9

Faria, Henrique Antonio Mendonça, and Valtencir Zucolotto. "Label-free electrochemical DNA biosensor for zika virus identification." Biosensors and Bioelectronics 131 (April 2019): 149–55. http://dx.doi.org/10.1016/j.bios.2019.02.018.

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Bae, Euiwon, Nan Bai, Amornrat Aroonnual, Arun K. Bhunia, and E. Daniel Hirleman. "Label-free identification of bacterial microcolonies via elastic scattering." Biotechnology and Bioengineering 108, no. 3 (November 10, 2010): 637–44. http://dx.doi.org/10.1002/bit.22980.

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11

Shin, Hyunku, Dongkwon Seo, and Yeonho Choi. "Extracellular Vesicle Identification Using Label-Free Surface-Enhanced Raman Spectroscopy: Detection and Signal Analysis Strategies." Molecules 25, no. 21 (November 9, 2020): 5209. http://dx.doi.org/10.3390/molecules25215209.

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Extracellular vesicles (EVs) have been widely investigated as promising biomarkers for the liquid biopsy of diseases, owing to their countless roles in biological systems. Furthermore, with the notable progress of exosome research, the use of label-free surface-enhanced Raman spectroscopy (SERS) to identify and distinguish disease-related EVs has emerged. Even in the absence of specific markers for disease-related EVs, label-free SERS enables the identification of unique patterns of disease-related EVs through their molecular fingerprints. In this review, we describe label-free SERS approaches for disease-related EV pattern identification in terms of substrate design and signal analysis strategies. We first describe the general characteristics of EVs and their SERS signals. We then present recent works on applied plasmonic nanostructures to sensitively detect EVs and notable methods to interpret complex spectral data. This review also discusses current challenges and future prospects of label-free SERS-based disease-related EV pattern identification.

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12

Lyu, Bohai, Wenfeng Gou, Feifei Xu, Yanli Li, Yiliang Li, and Wenbin Hou. "Label-free Protein Analysis Methods for Active Compound Targets Identification." Acta Chimica Sinica 82, no. 6 (2024): 629. http://dx.doi.org/10.6023/a24030082.

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13

Thomas, Giju, Melanie A. McWade, John Q. Nguyen, Melinda E. Sanders, James T. Broome, Naira Baregamian, Carmen C. Solórzano, and Anita Mahadevan-Jansen. "Innovative surgical guidance for label-free real-time parathyroid identification." Surgery 165, no. 1 (January 2019): 114–23. http://dx.doi.org/10.1016/j.surg.2018.04.079.

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14

Park, Hankum, Jaeyoung Ha, and Seung Bum Park. "Label-free target identification in drug discovery via phenotypic screening." Current Opinion in Chemical Biology 50 (June 2019): 66–72. http://dx.doi.org/10.1016/j.cbpa.2019.02.006.

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15

Poenar, Daniel P., Ciprian Iliescu, Jérôme Boulaire, and Hanry Yu. "Label-free virus identification and characterization using electrochemical impedance spectroscopy." ELECTROPHORESIS 35, no. 2-3 (November 27, 2013): 433–40. http://dx.doi.org/10.1002/elps.201300368.

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Wang, Shu, Xiuqiang Chen, Weilin Wu, Zhida Chen, Huiping Du, Xingfu Wang, Yu Vincent Fu, Liwen Hu, and Jianxin Chen. "Rapid, label-free identification of cerebellar structures using multiphoton microscopy." Journal of Biophotonics 10, no. 12 (May 2, 2017): 1617–26. http://dx.doi.org/10.1002/jbio.201600297.

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17

Schneider, Greg, Keisuke Wagatsuma, Asako Tsubouchi, Juliet Packiasamy, Mika Uematsu, Hiroko Nomaru, Kazuki Teranishi, et al. "Label-free morphological profiling and isolation of immune cell subsets using VisionSort, a novel, AI-based flow cytometry platform." Journal of Immunology 212, no. 1_Supplement (May 1, 2024): 0251_4944. http://dx.doi.org/10.4049/jimmunol.212.supp.0251.4944.

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Abstract Identification, characterization, and minimally invasive isolation of specific populations of human immune cells are critical for understanding and treating disease. Here we present data on label-free identification of three immune cell subsets by morphological profiling using VisionSort; a label-free, artificial intelligence (AI)-driven cellular analysis and sorting platform. By capturing single-cell digital phenotypes, we characterized mouse T-cells and generated ‘ground truth’ functional profiles for activated and non-activated T cells. A set of machine-learning derived classifiers was generated to identify these phenotypic classes in unlabeled T-cell subsets. The classifier showed an area under the curve (AUC) performance for differentiating between phenotypically defined T cell populations of 0.917. In addition, by using unsupervised machine learning, we were able to resolve activated and non-activated T cell populations label free, using morphological data alone. Using a similar approach, we show label-free differentiation/classification of B cells from plasma cells with an AUC score of 0.941 and M1 and M2 polarized macrophages with an AUC score of 0.878 +/- 0.002 (n=6). Here we report results on the use of a novel, label-free cytometry platform to characterize and isolate human immune cell subsets using morphological profiling and AI with applications for investigators in basic life sciences and drug developers in small molecule, antibody, and cell therapy R&D.

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18

Zamzami, Mazin, Samer Alamoudi, Abrar Ahmad, Hani Choudhry, Mohammad Imran Khan, Salman Hosawi, Gulam Rabbani, El-Sayed Shalaan, and Bassim Arkook. "Direct Identification of Label-Free Gram-Negative Bacteria with Bioreceptor-Free Concentric Interdigitated Electrodes." Biosensors 13, no. 2 (January 23, 2023): 179. http://dx.doi.org/10.3390/bios13020179.

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This investigation demonstrates an electrochemical method for directly identifying unlabeled Gram-negative bacteria without other additives or labeling agents. After incubation, the bacterial cell surface is linked to the interdigitated electrode through electroadsorption. Next, these cells are exposed to a potential difference between the two electrodes. The design geometry of an electrode has a significant effect on the electrochemical detection of Gram-negative bacteria. Therefore, electrode design geometry is a crucial factor that needs to be considered when designing electrodes for electrochemical sensing. They provide the area for the reaction and are responsible for transferring electrons from one electrode to another. This work aims to study the available design in the commercial market to determine the most suitable electrode geometry with a high detection sensitivity that can be used to identify and quantify bacterial cells in normal saline solutions. To work on detecting bacterial cells without the biorecognition element, we have to consider the microelectrode’s design, which makes it very susceptible to bacteria size. The concentration–dilution technique measures the effect of the concentration on label-free Gram-negative bacteria in a normal saline solution without needing bio-recognized elements for a fast screening evaluation. This method’s limit of detection (LOD) cannot measure concentrations less than 102 CFU/mL and cannot distinguish between live and dead cells. Nevertheless, this approach exhibited excellent detection performance under optimal experimental conditions and took only a few hours.

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19

Baik, Minyoung, Sanghoon Shin, Samir Kumar, Dongmin Seo, Inha Lee, Hyun Sik Jun, Ka-Won Kang, Byung Soo Kim, Myung-Hyun Nam, and Sungkyu Seo. "Label-Free CD34+ Cell Identification Using Deep Learning and Lens-Free Shadow Imaging Technology." Biosensors 13, no. 12 (November 21, 2023): 993. http://dx.doi.org/10.3390/bios13120993.

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Accurate and efficient classification and quantification of CD34+ cells are essential for the diagnosis and monitoring of leukemia. Current methods, such as flow cytometry, are complex, time-consuming, and require specialized expertise and equipment. This study proposes a novel approach for the label-free identification of CD34+ cells using a deep learning model and lens-free shadow imaging technology (LSIT). LSIT is a portable and user-friendly technique that eliminates the need for cell staining, enhances accessibility to nonexperts, and reduces the risk of sample degradation. The study involved three phases: sample preparation, dataset generation, and data analysis. Bone marrow and peripheral blood samples were collected from leukemia patients, and mononuclear cells were isolated using Ficoll density gradient centrifugation. The samples were then injected into a cell chip and analyzed using a proprietary LSIT-based device (Cellytics). A robust dataset was generated, and a custom AlexNet deep learning model was meticulously trained to distinguish CD34+ from non-CD34+ cells using the dataset. The model achieved a high accuracy in identifying CD34+ cells from 1929 bone marrow cell images, with training and validation accuracies of 97.3% and 96.2%, respectively. The customized AlexNet model outperformed the Vgg16 and ResNet50 models. It also demonstrated a strong correlation with the standard fluorescence-activated cell sorting (FACS) technique for quantifying CD34+ cells across 13 patient samples, yielding a coefficient of determination of 0.81. Bland–Altman analysis confirmed the model’s reliability, with a mean bias of −2.29 and 95% limits of agreement between 18.49 and −23.07. This deep-learning-powered LSIT offers a groundbreaking approach to detecting CD34+ cells without the need for cell staining, facilitating rapid CD34+ cell classification, even by individuals without prior expertise.

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20

Jiang, Yiyue, Cheng Lei, Atsushi Yasumoto, Hirofumi Kobayashi, Yuri Aisaka, Takuro Ito, Baoshan Guo, et al. "Label-free detection of aggregated platelets in blood by machine-learning-aided optofluidic time-stretch microscopy." Lab on a Chip 17, no. 14 (2017): 2426–34. http://dx.doi.org/10.1039/c7lc00396j.

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21

Hassanain, Waleed A., Frederick L. Theiss, and Emad L. Izake. "Label-free identification of Erythropoietin isoforms by surface enhanced Raman spectroscopy." Talanta 236 (January 2022): 122879. http://dx.doi.org/10.1016/j.talanta.2021.122879.

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22

Jo, YoungJu, JaeHwang Jung, Min-hyeok Kim, HyunJoo Park, Suk-Jo Kang, and YongKeun Park. "Label-free identification of individual bacteria using Fourier transform light scattering." Optics Express 23, no. 12 (June 8, 2015): 15792. http://dx.doi.org/10.1364/oe.23.015792.

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23

Alfonso-García, Alba, Tim D. Smith, Rupsa Datta, Thuy U. Luu, Enrico Gratton, Eric O. Potma, and Wendy F. Liu. "Label-free identification of macrophage phenotype by fluorescence lifetime imaging microscopy." Journal of Biomedical Optics 21, no. 4 (April 18, 2016): 046005. http://dx.doi.org/10.1117/1.jbo.21.4.046005.

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24

Yan, Zhongbo, Suman Dutta, Zirui Liu, Xinke Yu, Neda Mesgarzadeh, Feng Ji, Gal Bitan, and Ya-Hong Xie. "A Label-Free Platform for Identification of Exosomes from Different Sources." ACS Sensors 4, no. 2 (January 15, 2019): 488–97. http://dx.doi.org/10.1021/acssensors.8b01564.

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25

Zhao, Bin, Ning Liu, Lai Chen, Shuo Geng, Zhijin Fan, and Jihong Xing. "Direct label-free methods for identification of target proteins in agrochemicals." International Journal of Biological Macromolecules 164 (December 2020): 1475–83. http://dx.doi.org/10.1016/j.ijbiomac.2020.07.237.

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26

Rukes, Verena, Evita Norkute, Georges Barnikol, Jingze Duan, Jiajie Gao, and Chan Cao. "BPS2025 – Label-free identification of full-length proteins using a nanopore." Biophysical Journal 124, no. 3 (February 2025): 497a. https://doi.org/10.1016/j.bpj.2024.11.2617.

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27

Park, Hankum, Jaeyoung Ha, Ja Young Koo, Jongmin Park, and Seung Bum Park. "Label-free target identification using in-gel fluorescence difference via thermal stability shift." Chemical Science 8, no. 2 (2017): 1127–33. http://dx.doi.org/10.1039/c6sc03238a.

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28

Puangpila, Chanida, and Ziad El Rassi. "Capturing and identification of differentially expressed fucome by a gel free and label free approach." Journal of Chromatography B 989 (May 2015): 112–21. http://dx.doi.org/10.1016/j.jchromb.2015.03.006.

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29

Lee, Do-Hyun, Xuan Li, Ning Ma, Michelle A. Digman, and Abraham P. Lee. "Rapid and label-free identification of single leukemia cells from blood in a high-density microfluidic trapping array by fluorescence lifetime imaging microscopy." Lab on a Chip 18, no. 9 (2018): 1349–58. http://dx.doi.org/10.1039/c7lc01301a.

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30

Nallala, Jayakrupakar, Marie-Danièle Diebold, Cyril Gobinet, Olivier Bouché, Ganesh Dhruvananda Sockalingum, Olivier Piot, and Michel Manfait. "Infrared spectral histopathology for cancer diagnosis: a novel approach for automated pattern recognition of colon adenocarcinoma." Analyst 139, no. 16 (2014): 4005–15. http://dx.doi.org/10.1039/c3an01022h.

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31

Martinez-Duarte, Rodrigo. "Editorial for the Special Issue on Micromachines for Dielectrophoresis." Micromachines 13, no. 3 (March 8, 2022): 417. http://dx.doi.org/10.3390/mi13030417.

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32

Dalmay, Claire, Arnaud Pothier, Mathilde Cheray, Fabrice Lalloue, Marie-Odile Jauberteau, and Pierre Blondy. "Label-free RF biosensors for human cell dielectric spectroscopy." International Journal of Microwave and Wireless Technologies 1, no. 6 (December 2009): 497–504. http://dx.doi.org/10.1017/s1759078709990614.

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This paper presents an original biosensor chip allowing determination of intrinsic relative permittivity of biological cells at microwave frequencies. This sensor permits non-invasive cell identification and discrimination using an RF signal to probe intracellular medium of biological samples. Indeed, these sensors use an RF planar resonator that allows detection capabilities on less than 10 cells, thanks to the microscopic size of its sensitive area. Especially, measurements between 15 and 35 GHz show the ability label-free biosensors to differentiate two human cell types using their own electromagnetic characteristics. The real part of permittivity of cells changes from 20 to 48 for the nervous system cell types studied. The proposed biodetection method is detailed and we show how the accuracy and the repeatability of measurements have been improved to reach reproducible measurements.

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33

M. Santhosh, Neelakandan, Vasyl Shvalya, Martina Modic, Nataša Hojnik, Janez Zavašnik, Jaka Olenik, Martin Košiček, Gregor Filipič, Ibrahim Abdulhalim, and Uroš Cvelbar. "Label‐Free Mycotoxin Raman Identification by High‐Performing Plasmonic Vertical Carbon Nanostructures." Small 17, no. 49 (October 11, 2021): 2103677. http://dx.doi.org/10.1002/smll.202103677.

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Choi, Junseo, Zheng Jia, Ramin Riahipour, Collin J. McKinney, Charuni A. Amarasekara, Kumuditha M. Weerakoon‐Ratnayake, Steven A. Soper, and Sunggook Park. "Label‐Free Identification of Single Mononucleotides by Nanoscale Electrophoresis (Small 42/2021)." Small 17, no. 42 (October 2021): 2170220. http://dx.doi.org/10.1002/smll.202170220.

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35

Liu, Xinlu, Na Li, Chi Zhang, Xiaoyu Wu, Shoujia Zhang, Gang Dong, and Ge Liu. "Identification of metastasis-associated exoDEPs in colorectal cancer using label-free proteomics." Translational Oncology 19 (May 2022): 101389. http://dx.doi.org/10.1016/j.tranon.2022.101389.

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36

Wu, G., J. Wei, Z. Zheng, J. Ye, and S. Zeng. "Label-free identification of intestinal metaplasia in the stomach using multiphoton microscopy." Laser Physics Letters 11, no. 6 (April 16, 2014): 065602. http://dx.doi.org/10.1088/1612-2011/11/6/065602.

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37

Wang, Shu, Liwei Jiang, Huiping Du, Xingfu Wang, Liqin Zheng, Lianhuang Li, Shuangmu Zhuo, Xiaoqin Zhu, and Jianxin Chen. "Label-free identification of the hippocampus and surrounding structures by multiphoton microscopy." Laser Physics Letters 13, no. 5 (April 19, 2016): 055603. http://dx.doi.org/10.1088/1612-2011/13/5/055603.

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38

Danielsen, Heidi N., Susan H. Hansen, Florian-Alexander Herbst, Henrik Kjeldal, Allan Stensballe, Per H. Nielsen, and Morten S. Dueholm. "Direct Identification of Functional Amyloid Proteins by Label-Free Quantitative Mass Spectrometry." Biomolecules 7, no. 4 (August 4, 2017): 58. http://dx.doi.org/10.3390/biom7030058.

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39

Wang, Jue, Yang Luo, Bo Zhang, Ming Chen, Junfu Huang, Kejun Zhang, Weiyin Gao, Weiling Fu, Tianlun Jiang, and Pu Liao. "Rapid label-free identification of mixed bacterial infections by surface plasmon resonance." Journal of Translational Medicine 9, no. 1 (2011): 85. http://dx.doi.org/10.1186/1479-5876-9-85.

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Xin, Meiguo, Lin Zeng, Di Ran, Xiangmei Chen, Yang Xu, Daoxuan Shi, Yonghong He, and Suyi Zhong. "Label-free rapid identification of cooked meat using MIP-quantum weak measurement." Food and Agricultural Immunology 31, no. 1 (January 1, 2020): 317–28. http://dx.doi.org/10.1080/09540105.2020.1726879.

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41

van der Pol, Edwin, Leonie de Rond, Frank A. W. Coumans, Elmar L. Gool, Anita N. Böing, Auguste Sturk, Rienk Nieuwland, and Ton G. van Leeuwen. "Absolute sizing and label-free identification of extracellular vesicles by flow cytometry." Nanomedicine: Nanotechnology, Biology and Medicine 14, no. 3 (April 2018): 801–10. http://dx.doi.org/10.1016/j.nano.2017.12.012.

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42

Morales, Paula, Lauren S. Whyte, Roberto Chicharro, María Gómez-Cañas, M. Ruth Pazos, Pilar Goya, Andrew J. Irving, Javier Fernández-Ruiz, Ruth A. Ross, and Nadine Jagerovic. "Identification of Novel GPR55 Modulators Using Cell-Impedance-Based Label-Free Technology." Journal of Medicinal Chemistry 59, no. 5 (February 5, 2016): 1840–53. http://dx.doi.org/10.1021/acs.jmedchem.5b01331.

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43

Zhang, Chongsheng, Jingjun Bi, Changchang Liu, and Ke Chen. "A parameter-free label propagation algorithm for person identification in stereo videos." Neurocomputing 218 (December 2016): 72–78. http://dx.doi.org/10.1016/j.neucom.2016.08.069.

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Rollo, Enrica, Enrico Tenaglia, Raphaël Genolet, Elena Bianchi, Alexandre Harari, George Coukos, and Carlotta Guiducci. "Label-free identification of activated T lymphocytes through tridimensional microsensors on chip." Biosensors and Bioelectronics 94 (August 2017): 193–99. http://dx.doi.org/10.1016/j.bios.2017.02.047.

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45

Saxena, Chaitanya. "Identification of protein binding partners of small molecules using label-free methods." Expert Opinion on Drug Discovery 11, no. 10 (August 31, 2016): 1017–25. http://dx.doi.org/10.1080/17460441.2016.1227316.

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46

Lundstrom, Kenneth. "Cell-impedance-based label-free technology for the identification of new drugs." Expert Opinion on Drug Discovery 12, no. 4 (March 2017): 335–43. http://dx.doi.org/10.1080/17460441.2017.1297419.

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47

Niedieker, Daniel, Frederik GrosserÜschkamp, Anja Schreiner, Katalin Barkovits, Carsten Kötting, Katrin Marcus, Klaus Gerwert, and Matthias Vorgerd. "Label-free identification of myopathological features with coherent anti-Stokes Raman scattering." Muscle & Nerve 58, no. 3 (May 17, 2018): 456–59. http://dx.doi.org/10.1002/mus.26140.

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48

Cheong, Youjin, Young Jin Kim, Heeyoon Kang, Samjin Choi, and Hee Joo Lee. "Label-free identification of antibiotic resistant isolates of livingEscherichia coli: Pilot study." Microscopy Research and Technique 80, no. 2 (October 2, 2016): 177–82. http://dx.doi.org/10.1002/jemt.22785.

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49

Zhang, Qi‐Jie, Yang Chen, Xiao‐Huan Zou, Wei Hu, Min‐Lu Ye, Qi‐Fu Guo, Xue‐Liang Lin, Shang‐Yuan Feng, and Ning Wang. "Promoting identification of amyotrophic lateral sclerosis based on label‐free plasma spectroscopy." Annals of Clinical and Translational Neurology 7, no. 10 (September 19, 2020): 2010–18. http://dx.doi.org/10.1002/acn3.51194.

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50

Spitaleri, Andrea, Denis Garoli, Moritz Schütte, Hans Lehrach, Walter Rocchia, and Francesco De Angelis. "Adaptive nanopores: A bioinspired label-free approach for protein sequencing and identification." Nano Research 14, no. 1 (September 30, 2020): 328–33. http://dx.doi.org/10.1007/s12274-020-3095-z.

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Abstract:

AbstractSingle molecule protein sequencing would tremendously impact in proteomics and human biology and it would promote the development of novel diagnostic and therapeutic approaches. However, its technological realization can only be envisioned, and huge challenges need to be overcome. Major difficulties are inherent to the structure of proteins, which are composed by several different amino-acids. Despite long standing efforts, only few complex techniques, such as Edman degradation, liquid chromatography and mass spectroscopy, make protein sequencing possible. Unfortunately, these techniques present significant limitations in terms of amount of sample required and dynamic range of measurement. It is known that proteins can distinguish closely similar molecules. Moreover, several proteins can work as biological nanopores in order to perform single molecule detection and sequencing. Unfortunately, while DNA sequencing by means of nanopores is demonstrated, very few examples of nanopores able to perform reliable protein-sequencing have been reported so far. Here, we investigate, by means of molecular dynamics simulations, how a re-engineered protein, acting as biological nanopore, can be used to recognize the sequence of a translocating peptide by sensing the “shape” of individual amino-acids. In our simulations we demonstrate that it is possible to discriminate with high fidelity, 9 different amino-acids in a short peptide translocating through the engineered construct. The method, here shown for fluorescence-based sequencing, does not require any labelling of the peptidic analyte. These results can pave the way for a new and highly sensitive method of sequencing.

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