Cytometry Part A最新文献

{"title":"Beyond PBMCs: Polymer-Based Cell Mimics for Robust TBNK Immunophenotyping Assay Validation.","authors":"Swetha Pratyusha Gunturu, Subhanip Biswas, Armando Martinez, Brian Kerfs, Kanwal Palla, Louisa D'Lima","doi":"10.1002/cyto.a.70031","DOIUrl":"https://doi.org/10.1002/cyto.a.70031","url":null,"abstract":"<p><p>Flow cytometry-based TBNK immunophenotyping is widely used to assess immune status in research, clinical diagnostics, and cell therapy development. Biological control materials such as PBMCs often serve as physiologically relevant controls, but suffer from considerable variability due to donor differences, limited stability, and fluctuations in antigen expression levels between lots. These factors make it challenging to achieve consistent assay performance, especially in longitudinal or multi-site environments. TBNK Cell Mimic (synthetic controls in PhenoCyte product line, developed by Slingshot Biosciences; henceforth referred to as TBNK Cell Mimic in this manuscript) are polymer-based cell mimics engineered to provide defined scatter properties, controlled antigen density, and stable subset ratios. Their scatter profiles are designed to be biologically relevant and comparable to those of native leukocyte populations. In this study, we performed a comprehensive analytical validation of a TBNK immunophenotyping assay using these TBNK Cell Mimics as standardized reference controls. Validation parameters included repeatability, intermediate precision, accuracy, linearity, specificity, robustness, stability, and carryover. The TBNK Cell Mimic met all predefined acceptance criteria, demonstrating ≤ 5% CV for intra- and inter-assay precision and R² values > 0.998 across the linearity range. Accelerated stability studies performed at 25°C and 37°C showed < 5% variation in population frequencies, supporting the material's suitability for extended quality monitoring. These results indicate that TBNK Cell Mimic provides a consistent and reproducible reference material that can support assay validation and routine performance assessment. While not a replacement for biological samples when evaluating donor-specific or viability-dependent biology, their stability and lot-to-lot consistency offer a practical tool for reducing technical variability and improving harmonization across instruments, operators, and testing sites.</p>","PeriodicalId":11068,"journal":{"name":"Cytometry Part A","volume":" ","pages":""},"PeriodicalIF":2.1,"publicationDate":"2026-05-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147834978","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Modular and Scalable FPGA Platform for Intelligent, High-Throughput Image-Activated Cell Sorting.","authors":"Yan Ding, Jiehua Zhou, Ruiqi Xi, Xun Liu, Xiao Ma, Xiaolan Ruan, Du Wang, Guoxing Zheng, Long Xiao, Cheng Lei","doi":"10.1002/cyto.a.70033","DOIUrl":"https://doi.org/10.1002/cyto.a.70033","url":null,"abstract":"<p><p>Image-activated cell sorting (IACS) enables high-throughput cell classification by linking cellular morphology to physiology. While integrating advanced artificial intelligence (AI) can enhance the capture of subtle morphological heterogeneity, AI models inevitably introduce greater computational complexity when addressing complex problems, leading to increased analysis latency and latency instability. High latency implies longer chip lengths, while latency instability leads to incorrect sorting timing. To address this challenge, IACS can utilize field-programmable gate array (FPGA) as a stable, low-latency image analysis tool. Here, we developed a two-stage FPGA processing system for cellular data acquisition and real-time AI inference, utilizing the high-level synthesis (HLS) framework. By deploying a customized U-Net model on the AMD-Xilinx accelerator card and integrating hardware acceleration modules including activation function approximation and pixel-level convolution acceleration, we achieved a stable segmentation latency of 3.06 milliseconds (ms) at a 272 MHz clock frequency and delivered a processing throughput of up to 16,601 frames per second (fps). Using the morphological parameters obtained after segmentation, we successfully separated deformed HeLa cells from normal cells and distinguished colorectal cells, red blood cells, HeLa cells, and microspheres. This work provides IACS with a stable and low-latency image processing solution.</p>","PeriodicalId":11068,"journal":{"name":"Cytometry Part A","volume":" ","pages":""},"PeriodicalIF":2.1,"publicationDate":"2026-05-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147834951","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Graph-Theoretic Approach to Minimizing Union Operations for Optimal FMO Construction.","authors":"Ryan Kmet, Shuchan Zhou, David Novo","doi":"10.1002/cyto.a.70032","DOIUrl":"https://doi.org/10.1002/cyto.a.70032","url":null,"abstract":"<p><p>We model the process of computing all <math> <semantics> <mrow> <mfenced><mrow><mi>n</mi> <mo>-</mo> <mn>1</mn></mrow> </mfenced> </mrow> <annotation>$$ left(n-1right) $$</annotation></semantics> </math> -element subsets of an <math> <semantics><mrow><mi>n</mi></mrow> <annotation>$$ n $$</annotation></semantics> </math> -element set (representing a set of fluorescence minus one [FMO] controls) using only binary union operations. By representing this process as a Directed Acyclic Graph (DAG), we present an algorithm that can make all FMOs in the theoretical minimum <math> <semantics><mrow><mn>3</mn> <mi>n</mi> <mo>-</mo> <mn>6</mn></mrow> <annotation>$$ 3n-6 $$</annotation></semantics> </math> unions (when <math> <semantics><mrow><mi>n</mi> <mo>≥</mo> <mn>3</mn></mrow> <annotation>$$ nge 3 $$</annotation></semantics> </math> ). Finally, we generalize the construction to arbitrary subsets of leave-one-out targets and prove a bound on the number of operations in such cases.</p>","PeriodicalId":11068,"journal":{"name":"Cytometry Part A","volume":" ","pages":""},"PeriodicalIF":2.1,"publicationDate":"2026-05-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147811876","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Development and Validation of Flow Cytometry-Based Method to Quantify CD34/CD45+ Cells as a Release Criterion of Clinical-Grade Products for a Phase III Clinical Trial.","authors":"Delphine Bouis, Aurélien Goubaud, Arthur Cormier, Sara Bennaoum, Manon Destalminil, Hélène Schaffhauser, Christine Vignon, Matthieu de Kalbermatten, Ibon Garitaonandia","doi":"10.1002/cyto.a.70028","DOIUrl":"https://doi.org/10.1002/cyto.a.70028","url":null,"abstract":"<p><p>We completed an international, multicenter, randomized, open-label Phase I/IIb trial assessing the safety and preliminary efficacy of transendocardial injection of autologous expanded CD34+ cells (ProtheraCytes) in patients after acute myocardial infarction (AMI; NCT02669810). A multicenter, randomized, controlled Phase III study is now being initiated. To support release of ProtheraCytes clinical batches, we validated two flow cytometry methods for accurate quantification of CD34/CD45⁺ cells (stem cell enumeration-SCE method) and characterization of accessory leukocyte subsets (monocytes, granulocytes, and B, T, NK lymphocytes-accessory populations immunophenotyping method). All the recovery rates for both methods, with calculations derived from QC materials specifications, met the acceptance criteria, based on precision assessment according to ICH Q2(R2), European Pharmacopeia (Ph. Eur. 2.7.23 and Ph. Eur. 2.7.24), and ISHAGE guidelines. In addition, the precision results (repeatability and intermediate precision) were lower than 28.3% (≤ 30% for accessory populations immunophenotyping method) and lower than 13.5% (≤ 25% for SCE method). Finally, a perfect linearity was demonstrated for SCE method across 1.7-2622.5 cells/μL with coefficient of determination (R²) of linear regression above 0.99 and matrix effects nearly negligible for both methods. The specificity, precision and accuracy of these methods were proven in the analysis of six determinations per operator in three different series. Altogether, these results indicate a good accuracy and precision of the proposed methods determining absolute counts, viability, and proportions of live CD34/CD45+ cells and accessory populations. This validated flow cytometry assay will be implemented for release testing in the forthcoming Phase III clinical trial of ProtheraCytes in post-AMI patients.</p>","PeriodicalId":11068,"journal":{"name":"Cytometry Part A","volume":" ","pages":""},"PeriodicalIF":2.1,"publicationDate":"2026-05-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147811820","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Optimizing Cell Sorting Performance: A Comparative Study Utilizing Spectral Flow Cytometry.","authors":"Jarina Pena DaMata, Randall Johnson, Iyadh Douagi","doi":"10.1002/cyto.a.70025","DOIUrl":"https://doi.org/10.1002/cyto.a.70025","url":null,"abstract":"<p><p>The introduction of full spectral technology in flow cytometry has facilitated access to an increasing number of markers to define cell subsets with higher precision. Cell sorting has a unique advantage to combine high throughput single cell analysis and recovery of rare live single cells for further downstream multi-omics analysis. Many studies have focused on advancing high dimensional single cell analysis; however, strategies to maximize cell sorting recovery in the context of deep immunophenotyping remain poorly defined. In this study, we evaluated sort performance in a six-way simultaneous cell sort setup. We modified a protocol using counting beads to assess absolute count in different sort decision criteria or modes. We demonstrate that the number of events collected can vary as much as 20% from the values indicated by the sort counter and is dependent on sort mode. Using the absolute count assay, we confirmed optimal conditions for six-way sorting of diverse human peripheral blood cell subsets defined by a 35-color panel and delineated pitfalls that can ultimately lead to suboptimal yield. Together, these findings provide novel insights into optimization of sort performance for advanced sorting and introduce a new approach for refining strategies for the simultaneous isolation of complex or rare cell subsets.</p>","PeriodicalId":11068,"journal":{"name":"Cytometry Part A","volume":" ","pages":""},"PeriodicalIF":2.1,"publicationDate":"2026-04-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147765303","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"CytoBatchFlagR: A Comprehensive Framework to Objectively Assess High-Parameter Cytometry Data for Batch Effects.","authors":"Shruti Eswar, Zachary T Koenig, Amanda R Tursi, José Cobeña-Reyes, Tamara Tilburgs, Sandra Andorf","doi":"10.1002/cyto.a.70024","DOIUrl":"https://doi.org/10.1002/cyto.a.70024","url":null,"abstract":"<p><p>Rapid advancements in mass and flow cytometry technologies have allowed researchers to generate and analyze high-dimensional single cell datasets, often utilizing upwards of 40 protein markers. Such high-parameter cytometry is increasingly used in longitudinal immunological studies, but technical variations across experimental batch runs can confound biological signals. To mitigate the impact on downstream analyses, many studies include reference control samples in every run, and several approaches exist to adjust for batch effects. However, tools that objectively identify problematic batches and markers present within a dataset are limited. We introduce CytoBatchFlagR, a comprehensive and interpretable tool designed to flag batch-related problems at the marker and cell cluster level based on robust statistical evaluations. Batch and marker variations are assessed based on median signal intensities of negative and positive cell populations and positive cell frequencies, along with Earth Mover's Distance (EMD) of signal intensity distributions. Additionally, CytoBatchFlagR identifies cell type specific batch problems via unsupervised clustering. The tool is suitable for mass and flow cytometry datasets where it objectively detects distinct types of batch issues. We developed and tested CytoBatchFlagR using three cytometry datasets to demonstrate its utility and performance. We also demonstrated CytoBatchFlagR's effectiveness in assessing datasets that include or lack reference controls. CytoBatchFlagR improves quality control by enabling objective identification of technical variations that may impact downstream analysis in high-parameter cytometry data. The tool uses a series of complementary metrics to identify potential batch-related problems at the marker and cell population level and presents the results through interpretable visualizations. This allows users to make informed decisions about whether to apply batch correction or exclude specific batches or markers from downstream analyses. CytoBatchFlagR is freely available as R scripts, with documentation and a tutorial to help users get started.</p>","PeriodicalId":11068,"journal":{"name":"Cytometry Part A","volume":" ","pages":""},"PeriodicalIF":2.1,"publicationDate":"2026-04-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147671008","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Automated Gating of CD34\u0000 + Cells in Cord Blood: Performance Evaluation of a Machine Learning-Based ISHAGE Protocol","authors":"Carl Simard,&nbsp;Diane Fournier,&nbsp;Patrick Trépanier","doi":"10.1002/cyto.a.70017","DOIUrl":"10.1002/cyto.a.70017","url":null,"abstract":"<div>\u0000 \u0000 <p>Precise quantification of cellular subsets is fundamental for qualifying grafts and supporting emerging therapies. CD34⁺ enumeration in cord blood using the ISHAGE protocol exemplifies the operator variability inherent to manual gating. We evaluated whether a machine-learning approach could provide standardized automated enumeration and reduce variability. A machine-learning–based automatic gating algorithm was trained on 29 manually gated FCS files and applied to raw flow cytometry data. Performance was compared with manual gating from nine laboratories from a previously published multicenter study using <i>Z</i>-scores, rank positioning, absolute deviation, correlations, Bland–Altman analysis, and intraclass correlation coefficients. Across 12 samples, AI1 remained within ± 2 SD of the human consensus in all cases, whereas AI2 exceeded this threshold in two. AI1 consistently ranked closer to the human median and showed narrower deviations. Both models correlated strongly with manual gating (AI1: <i>r</i> = 0.991; AI2: <i>r</i> = 0.968). Bland–Altman analysis showed minimal bias and narrow limits of agreement for AI1 versus its human reference, while AI2 and human–human comparisons displayed greater variability. ICCs indicated high reliability across all comparisons, with the strongest agreement observed for AI1 versus Lab1 (ICC = 0.995). A machine learning–based automatic gating approach can reproduce expert CD34⁺ enumeration with high fidelity. By reducing operator-dependent variability, this method may strengthen cytometry standardization across cord blood banking and broader cellular therapy workflows.</p>\u0000 </div>","PeriodicalId":11068,"journal":{"name":"Cytometry Part A","volume":"109 2","pages":"108-114"},"PeriodicalIF":2.1,"publicationDate":"2026-03-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146257580","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Imaging Flow Cytometry Detection of Cytogenetic Abnormalities in Circulating CD34+ Cells Predicts Leukemic Transformation in Myelofibrosis","authors":"Ruby M. Hamilton,&nbsp;Ryan J. Collinson,&nbsp;Henry Y. Hui,&nbsp;Zi Yun Ng,&nbsp;Hun S. Chuah,&nbsp;Malcolm Webb,&nbsp;Belinda B. Guo,&nbsp;Wendy N. Erber,&nbsp;Kathy A. Fuller","doi":"10.1002/cytoa.70012","DOIUrl":"10.1002/cytoa.70012","url":null,"abstract":"<p>Myelofibrosis is a myeloproliferative neoplasm with potential to transform to acute myeloid leukemia. This evolution is unpredictable and current assays lack the sensitivity and applicability needed to predict this transformation. While population-level data utilizing comprehensive genomic profiling can identify subgroups at higher risk of progression, they do not provide individualized information on the likelihood or timing of leukemia. Cytogenetic alterations are typically present in secondary leukemia. We aimed to determine whether these changes could be detected at an early stage. To achieve this we established and tested a single-cell imaging flow cytometric method for chromosomal aberrations using fluorescence in situ hybridization (FISH) probes to analyze circulating CD34/CD45-positive cells. Peripheral blood samples from 14 patients, collected at up to eight timepoints over a 34-month period, were analyzed for defects involving chromosomes 1, 5, and 17. Following cell immunophenotyping and FISH probe hybridization, a mean of 174,216 mononuclear cells was assessed per sample. Chromosomal abnormalities including gain(1q), del(5q), idic (5), monosomy 17, and/or del(17p) were identified in eight patients, at frequencies down to 0.2% of mononuclear cells. Serial analyses revealed emergence of new chromosomal lesions, clonal evolution, dominance, and multi-hit abnormalities. In three patients, acquired chromosome 17 abnormalities preceded progression to secondary leukemia by up to 7 months. This pilot study demonstrates that imaging flow cytometry-based FISH of circulating CD34/CD45-positive cells enables real-time, blood-based surveillance for cytogenetic evolution in myelofibrosis. The ability to dynamically track clone size and hierarchy highlights its potential as an early predictor of leukemic transformation in myelofibrosis.</p>","PeriodicalId":11068,"journal":{"name":"Cytometry Part A","volume":"109 2","pages":"98-107"},"PeriodicalIF":2.1,"publicationDate":"2026-03-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/cytoa.70012","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146112421","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Label-Free Holographic Imaging Flow Cytometry With Deep-Learning-Based Detection and Classification of Thousands of Cells Per Second","authors":"Dana Yagoda-Aharoni,&nbsp;Eden Dotan,&nbsp;Matan Dudaie,&nbsp;Natan T. Shaked","doi":"10.1002/cytoa.70008","DOIUrl":"10.1002/cytoa.70008","url":null,"abstract":"<p>We present a new end-to-end neural network approach for real-time biological cell detection and classification via label-free quantitative imaging flow cytometry based on digital holography, offering a comprehensive representation of cellular structures without the need for chemical cell staining. In contrast to previous studies, our method is the first to obtain classification and detection of cells, imaged during flow using large-magnification microscopy, in 0.44 msec, allowing real-time label-free imaging flow cytometry, with more than 10× speedup compared to YOLOv8n. The custom-made two-stage neural network consists of fixed convolution layers using image processing filters to detect a single location per object, followed by two convolutional layers that classify each detected cell. This approach enables reducing computational complexity and offers high-throughput, label-free imaging-based analysis suitable for real-time imaging flow cytometry. We validate the method on two cell datasets, T-cells at different activation stages and cancer cells of different metastatic potentials, demonstrating the method's adaptability. Our results show the ability to image, detect, and classify thousands of cells per second during flow, highlighting the potential of label-free imaging flow cytometry for real-time cell monitoring, early disease detection, and high-speed diagnostics.</p>","PeriodicalId":11068,"journal":{"name":"Cytometry Part A","volume":"109 2","pages":"89-97"},"PeriodicalIF":2.1,"publicationDate":"2026-03-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/cytoa.70008","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146050817","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"GeoMx and RNAscope: A Comparative Assessment of Their Utility for Spatial mRNA Expression Profiling in Formalin-Fixed Breast Cancer Tissue","authors":"Christian Thomsen,&nbsp;Birgit Truumees,&nbsp;Søren Nielsen,&nbsp;Boye Schnack Nielsen,&nbsp;Sara Rose Newell Jensen,&nbsp;Kasper Thorsen,&nbsp;Rasmus Røge","doi":"10.1002/cyto.a.70020","DOIUrl":"10.1002/cyto.a.70020","url":null,"abstract":"<p>mRNA expression analysis in formalin-fixed tissue is essential for many biomarker and tumor microenvironment studies. NanoString GeoMx Digital Spatial Profiler is a recent technique that offers high-plex spatial transcriptomics (up to 18,000 genes), while RNAscope (1–4 plex) is a well-established mRNA in situ hybridization method. This study compares quantitative expression estimates obtained by GeoMX and RNAscope. Serial sections from two TMAs containing mammary cancers, including triple-negative (<i>n</i> = 48) and varying ER/HER2 status (<i>n</i> = 45), were analyzed using GeoMx Cancer Transcriptomics Atlas (CTA) covering approximately 1,800 genes and RNAscope probes for GATA3, SOX10, and PD-L1 mRNAs. Expression was quantified as counts/cell (GeoMx, geometric mean of five probes per gene) and average dots/cell (RNAscope, QuPath). Positivity was defined as above the limit of quantification (GeoMx) or &gt; 0.1 dots/cell (RNAscope). High correlation was observed for GATA3 (<i>R</i> = 0.87) and SOX10 (<i>R</i> = 0.77) between methods. PD-L1 expression was high in only one core, precluding correlation analysis. RNAscope demonstrated higher sensitivity and a broader dynamic range than GeoMx. In conclusion, GeoMx CTA and RNAscope exhibit a strong correlation. GeoMx enabled highly multiplexed gene expression analysis, whereas RNAscope provided better sensitivity and single-cell resolution. The choice of method should be guided by study objectives.</p>","PeriodicalId":11068,"journal":{"name":"Cytometry Part A","volume":"109 2","pages":"147-154"},"PeriodicalIF":2.1,"publicationDate":"2026-03-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/cyto.a.70020","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147376455","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}