Trisna Julian, Tao Tang, Yoichiroh Hosokawa, Yaxiaer Yalikun
{"title":"Machine learning implementation strategy in imaging and impedance flow cytometry.","authors":"Trisna Julian, Tao Tang, Yoichiroh Hosokawa, Yaxiaer Yalikun","doi":"10.1063/5.0166595","DOIUrl":null,"url":null,"abstract":"<p><p>Imaging and impedance flow cytometry is a label-free technique that has shown promise as a potential replacement for standard flow cytometry. This is due to its ability to provide rich information and archive high-throughput analysis. Recently, significant efforts have been made to leverage machine learning for processing the abundant data generated by those techniques, enabling rapid and accurate analysis. Harnessing the power of machine learning, imaging and impedance flow cytometry has demonstrated its capability to address various complex phenotyping scenarios. Herein, we present a comprehensive overview of the detailed strategies for implementing machine learning in imaging and impedance flow cytometry. We initiate the discussion by outlining the commonly employed setup to acquire the data (i.e., image or signal) from the cell. Subsequently, we delve into the necessary processes for extracting features from the acquired image or signal data. Finally, we discuss how these features can be utilized for cell phenotyping through the application of machine learning algorithms. Furthermore, we discuss the existing challenges and provide insights for future perspectives of intelligent imaging and impedance flow cytometry.</p>","PeriodicalId":8855,"journal":{"name":"Biomicrofluidics","volume":"17 5","pages":"051506"},"PeriodicalIF":2.6000,"publicationDate":"2023-10-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10613093/pdf/","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Biomicrofluidics","FirstCategoryId":"5","ListUrlMain":"https://doi.org/10.1063/5.0166595","RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2023/9/1 0:00:00","PubModel":"eCollection","JCR":"Q2","JCRName":"BIOCHEMICAL RESEARCH METHODS","Score":null,"Total":0}
引用次数: 0
Abstract
Imaging and impedance flow cytometry is a label-free technique that has shown promise as a potential replacement for standard flow cytometry. This is due to its ability to provide rich information and archive high-throughput analysis. Recently, significant efforts have been made to leverage machine learning for processing the abundant data generated by those techniques, enabling rapid and accurate analysis. Harnessing the power of machine learning, imaging and impedance flow cytometry has demonstrated its capability to address various complex phenotyping scenarios. Herein, we present a comprehensive overview of the detailed strategies for implementing machine learning in imaging and impedance flow cytometry. We initiate the discussion by outlining the commonly employed setup to acquire the data (i.e., image or signal) from the cell. Subsequently, we delve into the necessary processes for extracting features from the acquired image or signal data. Finally, we discuss how these features can be utilized for cell phenotyping through the application of machine learning algorithms. Furthermore, we discuss the existing challenges and provide insights for future perspectives of intelligent imaging and impedance flow cytometry.
期刊介绍:
Biomicrofluidics (BMF) is an online-only journal published by AIP Publishing to rapidly disseminate research in fundamental physicochemical mechanisms associated with microfluidic and nanofluidic phenomena. BMF also publishes research in unique microfluidic and nanofluidic techniques for diagnostic, medical, biological, pharmaceutical, environmental, and chemical applications.
BMF offers quick publication, multimedia capability, and worldwide circulation among academic, national, and industrial laboratories. With a primary focus on high-quality original research articles, BMF also organizes special sections that help explain and define specific challenges unique to the interdisciplinary field of biomicrofluidics.
Microfluidic and nanofluidic actuation (electrokinetics, acoustofluidics, optofluidics, capillary)
Liquid Biopsy (microRNA profiling, circulating tumor cell isolation, exosome isolation, circulating tumor DNA quantification)
Cell sorting, manipulation, and transfection (di/electrophoresis, magnetic beads, optical traps, electroporation)
Molecular Separation and Concentration (isotachophoresis, concentration polarization, di/electrophoresis, magnetic beads, nanoparticles)
Cell culture and analysis(single cell assays, stimuli response, stem cell transfection)
Genomic and proteomic analysis (rapid gene sequencing, DNA/protein/carbohydrate arrays)
Biosensors (immuno-assay, nucleic acid fluorescent assay, colorimetric assay, enzyme amplification, plasmonic and Raman nano-reporter, molecular beacon, FRET, aptamer, nanopore, optical fibers)
Biophysical transport and characterization (DNA, single protein, ion channel and membrane dynamics, cell motility and communication mechanisms, electrophysiology, patch clamping). Etc...