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Biosensors
Special Issues
Advanced Cell-Analyzing Technologies and Their Biosensing Applications
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Advanced Cell-Analyzing Technologies and Their Biosensing Applications

A special issue of Biosensors (ISSN 2079-6374). This special issue belongs to the section "Biosensor and Bioelectronic Devices".

Deadline for manuscript submissions: 28 February 2026 | Viewed by 3440

Special Issue Editors

Prof. Dr. Tiean Zhou

E-Mail Website
Guest Editor
1. College of Bioscience and Biotechnology, Hunan Agricultural University, Changsha 410128, China
2. Hunan Provincial Engineering Technology Research Center for Cell Mechanics and Functional Analysis, Changsha 410128, China
Interests: developing novel cytomechanical methods and technologies for: the characterization and study of cellular structure and function; applications in biomedicine and modern agriculture focusing on drug efficacy and toxicity assessments, as well as evaluations of resistances to stresses in crops; designing and applications of living cell/sensing interfaces; development of cell mechanics chips and instruments
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Linhong Deng

E-Mail Website
Guest Editor
1. Institute of Biomedical Engineering and Health Sciences, School of Medical and Health Engineering, Changzhou University, Changzhou 213164, China
2. Hunan Provincial Engineering Technology Research Center for Cell Mechanics and Functional Analysis, Changsha 410128, China
Interests: cell mechanics and microrheology; structure and function of airway smooth muscle; cellular and molecular phathophysiology of asthma; respiratory biomedical engineering; airway organoids and lab-on-chip technology; cell mechanics-based drug discovery for pulmonary medicine
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Recent breakthroughs in cell analysis and biosensing technologies have significantly advanced our understanding of cellular behaviors, disease mechanisms, and personalized medicine. This Special Issue focuses on cutting-edge innovations in cell-based sensing, high-throughput screening, and single-cell analysis, highlighting their transformative potential in biomedical research, diagnostics, and therapeutics.

We invite contributions that focus on advanced cell-analyzing technologies and biosensing applications across multiple scales, from the single cell to cell populations and organoids. These may cover, but are not limited to, the following topics:

  • Microfluidics and Lab-on-a-Chip Systems: Miniaturized platforms for the sorting, trapping, and real-time monitoring of cells.
  • Biosensor Integration: Optical, electrochemical, and mechanical biosensors for dynamic cell behavior studies.
  • Organ/organoids-on-a-Chip and 3D Cell Culture Devices: Biomimetic systems that use biosensing technologies for drug screening, disease modeling, and real-time cellular response monitoring.
  • AI and Machine Learning in Cell Analysis and Biosensing: Automated image analysis, biosensors with data interpretation, and predictive modeling for enhanced cell behavior analyses and diagnostics.
  • Point-of-Care Diagnostics: Portable and wearable biosensors for rapid cell-based health monitoring.
  • Nanotechnology in Cell Sensing: The use of nanomaterials for enhanced sensitivity and multiplexed detection.

This Special Issue welcomes original research articles, reviews, and perspectives addressing current challenges and technological innovations in cell analysis and biosensing. We especially welcome manuscripts involving cell mechanics analyzing techniques toward the goals of quantifying cellular function, phenotype and behaviors, tracking complex biological processes associated with health and disease of biological systems. We encourage contributions from researchers across engineering, materials science, biophysics, and related disciplines in order to showcase novel methodologies and applications emerging from this rapidly evolving field. This Special Issue aims to highlight emerging technologies that push the boundaries of cellular analysis and biosensor development.

You may choose our Joint Special Issue in Cells.

Prof. Dr. Tiean Zhou
Prof. Dr. Linhong Deng
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 250 words) can be sent to the Editorial Office for assessment.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Biosensors is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2200 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • cell analysis
  • biosensors
  • single-cell technologies
  • microfluidics
  • organ-on-a-chip
  • AI-driven biosensing diagnostics
  • nanotechnology
  • cell mechanics
  • cellular function analysis
  • cell phenotype assays

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Further information on MDPI's Special Issue policies can be found here.

Published Papers (4 papers)

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14 pages, 1626 KB  
Article
Deep Learning-Based Prediction of Individual Cell α-Dispersion Capacitance from Morphological Features
by Tae Young Kang, Soojung Kim, Yoon-Hwae Hwang and Kyujung Kim
Biosensors 2025, 15(11), 753; https://doi.org/10.3390/bios15110753 - 10 Nov 2025
Viewed by 395
Abstract
The biophysical characteristics of cellular membranes, particularly their electrical properties in the α-dispersion frequency domain, offer valuable insights into cellular states and are increasingly important for cancer diagnostics through epidermal growth factor receptor (EGFR) expression analysis. However, a critical limitation in these [...] Read more.
The biophysical characteristics of cellular membranes, particularly their electrical properties in the α-dispersion frequency domain, offer valuable insights into cellular states and are increasingly important for cancer diagnostics through epidermal growth factor receptor (EGFR) expression analysis. However, a critical limitation in these electrical measurements is the confounding effect of morphological changes that inevitably occur during prolonged observation periods. These shape alterations significantly impact measured capacitance values, potentially masking true biological responses to epidermal growth factor (EGF) stimulation that are essential for cancer detection. In this study, we attempted to address this fundamental challenge by developing a deep learning method that establishes a direct computational relationship between cellular morphology and electrical properties. We combined optical trapping technology and capacitance measurements to generate a comprehensive dataset of HeLa cells under two different experimental conditions: (i) DPBS treatment and (ii) EGF stimulation. Our convolutional neural network (CNN) architecture accurately predicts 401-point capacitance spectra (0.1–2 kHz) from binary morphological images at low frequencies (0.1–0.8 kHz, < 10% error rate). This capability allows for the identification and subtraction of morphology-dependent components from measured capacitance changes, effectively isolating true biological responses from morphological artefacts. The model demonstrates remarkable prediction performance across diverse cell morphologies in both experimental conditions, validating the robust relationship between cellular shape and electrical characteristics. Our method significantly improves the precision and reliability of EGFR-based cancer diagnostics by providing a computational framework for a morphology-induced measurement error correction. Full article
(This article belongs to the Special Issue Advanced Cell-Analyzing Technologies and Their Biosensing Applications)
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14 pages, 2162 KB  
Article
Sensing Cellular Damages Induced by Food Safety Hazards Using Bacterial Stress-Responsive Biosensors
by Ruiqi Li, Manzhuan Lou, Wei He and Shu Quan
Biosensors 2025, 15(10), 695; https://doi.org/10.3390/bios15100695 - 14 Oct 2025
Viewed by 693
Abstract
Food safety hazards induce diverse cellular damages including DNA damage, oxidative stress, proteotoxic stress, and membrane disruption, ultimately contributing to various human diseases. Conventional toxicity assays, while effective, are often resource-intensive and lack the capacity to distinguish among these different damage types, thereby [...] Read more.
Food safety hazards induce diverse cellular damages including DNA damage, oxidative stress, proteotoxic stress, and membrane disruption, ultimately contributing to various human diseases. Conventional toxicity assays, while effective, are often resource-intensive and lack the capacity to distinguish among these different damage types, thereby limiting insight into toxic responses and the development of effective strategies for targeted risk mitigation. Here, we constructed a panel of Escherichia coli whole-cell biosensors capable of distinguishing distinct categories of cellular damage. Specifically, an optimized RecA-LexA-based DNA damage biosensor that precisely controls the exogenous expression of the transcriptional repressor LexA achieved a 35.5% reduction in baseline signal and a 36.6-fold induction of fluorescence. In parallel, systematic promoter screening identified Pfpr, PkatG, PgrpE, and PfabA as effective modules for constructing oxidative, proteotoxic, and membrane stress biosensors. These biosensors exhibited high specificity and sensitivity, generating dose-dependent responses to model toxicants and enabling discrimination of cellular damage induced by typical hazards such as norfloxacin and ciprofloxacin. Notably, the DNA damage biosensor detected norfloxacin with a limit of detection (LOD) of 1.3 ng/mL in standard solution and 3.0 ng/mL in milk, comparable to that of high-performance liquid chromatography (HPLC). Together, our work not only provides a versatile, cost-effective, and sensitive tool for assessing diverse cellular damages induced by food safety hazards, but also demonstrates potential utility for practical food safety monitoring. Full article
(This article belongs to the Special Issue Advanced Cell-Analyzing Technologies and Their Biosensing Applications)
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15 pages, 2880 KB  
Article
Double-Layered Microphysiological System Made of Polyethylene Terephthalate with Trans-Epithelial Electrical Resistance Measurement Function for Uniform Detection Sensitivity
by Naokata Kutsuzawa, Hiroko Nakamura, Laner Chen, Ryota Fujioka, Shuntaro Mori, Noriyuki Nakatani, Takahiro Yoshioka and Hiroshi Kimura
Biosensors 2025, 15(10), 663; https://doi.org/10.3390/bios15100663 - 2 Oct 2025
Viewed by 714
Abstract
Microphysiological systems (MPSs) have emerged as alternatives to animal testing in drug development, following the FDA Modernization Act 2.0. Double-layer channel-type MPS chips with porous membranes are widely used for modeling various organs, including the intestines, blood–brain barrier, renal tubules, and lungs. However, [...] Read more.
Microphysiological systems (MPSs) have emerged as alternatives to animal testing in drug development, following the FDA Modernization Act 2.0. Double-layer channel-type MPS chips with porous membranes are widely used for modeling various organs, including the intestines, blood–brain barrier, renal tubules, and lungs. However, these chips faced challenges owing to optical interference caused by light scattering from the porous membrane, which hinders cell observation. Trans-epithelial electrical resistance (TEER) measurement offers a non-invasive method for assessing barrier integrity in these chips. However, existing electrode-integrated MPS chips for TEER measurement have non-uniform current densities, leading to compromised measurement accuracy. Additionally, chips made from polydimethylsiloxane have been associated with drug absorption issues. This study developed an electrode-integrated MPS chip for TEER measurement with a uniform current distribution and minimal drug absorption. Through a finite element method simulation, electrode patterns were optimized and incorporated into a polyethylene terephthalate (PET)-based chip. The device was fabricated by laminating PET films, porous membranes, and patterned gold electrodes. The chip’s performance was evaluated using a perfused Caco-2 intestinal model. TEER levels increased and peaked on day 5 when cells formed a monolayer, and then they decreased with the development of villi-like structures. Concurrently, capacitance increased, indicating microvilli formation. Exposure to staurosporine resulted in a dose-dependent reduction in TEER, which was validated by immunostaining, indicating a disruption of the tight junction. This study presents a TEER measurement MPS platform with a uniform current density and reduced drug absorption, thereby enhancing TEER measurement reliability. This system effectively monitors barrier integrity and drug responses, demonstrating its potential for non-animal drug-testing applications. Full article
(This article belongs to the Special Issue Advanced Cell-Analyzing Technologies and Their Biosensing Applications)
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17 pages, 1568 KB  
Article
Cell-Free DNA Versus Circulating Tumor Cells: A Pilot Study of Alpha-Fetoprotein Analysis for Diagnosis and Treatment Monitoring in Hepatocellular Carcinoma
by Ga Young Moon, Hyun Sung Park, Ha Neul Kim, Hei-Gwon Choi, Yonghan Han, Hyuk Soo Eun, Tae Hee Lee and Jiyoon Bu
Biosensors 2025, 15(9), 579; https://doi.org/10.3390/bios15090579 - 4 Sep 2025
Cited by 1 | Viewed by 1180
Abstract
Serum alpha-fetoprotein (AFP) is widely used for hepatocellular carcinoma (HCC) management, yet its limited sensitivity and specificity restrict diagnostic and prognostic utility. In this study, we explore the clinical potential of AFP quantification from cell-free DNA (cfDNA) and circulating tumor cells (CTCs) using [...] Read more.
Serum alpha-fetoprotein (AFP) is widely used for hepatocellular carcinoma (HCC) management, yet its limited sensitivity and specificity restrict diagnostic and prognostic utility. In this study, we explore the clinical potential of AFP quantification from cell-free DNA (cfDNA) and circulating tumor cells (CTCs) using a novel bead-based liquid biopsy platform. Following isolation, AFP abundance in cfDNA was quantified by qPCR, while AFP protein expression in CTCs was assessed via immunohistochemistry. Compared to serum AFP, cfDNA-derived AFP demonstrated significantly greater diagnostic accuracy in distinguishing HCC patients from non-cancerous individuals (p < 0.0001, AUC = 0.998), while AFP+ CTCs showed high specificity. Post-treatment changes in AFP levels from cfDNA and CTCs were significantly associated with therapeutic response and overall survival, outperforming conventional serum AFP. Longitudinal monitoring further revealed that cfDNA AFP levels reliably captured recurrence events prior to clinical diagnosis. Moreover, a combined metric integrating AFP levels from cfDNA and CTCs significantly improved response stratification (AUC = 0.89), outperforming individual biomarkers. This pilot study highlights the potential of multimodal AFP profiling through cfDNA and CTCs as a promising, non-invasive approach for enhancing diagnosis, prognosis, and treatment monitoring in HCC, with direct implications for personalized therapeutic strategies. Full article
(This article belongs to the Special Issue Advanced Cell-Analyzing Technologies and Their Biosensing Applications)
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