Cytometry Part A最新文献

{"title":"Autofluorescence: From burden to benefit","authors":"Katherine R. Pilkington","doi":"10.1002/cyto.a.24885","DOIUrl":"10.1002/cyto.a.24885","url":null,"abstract":"<p>With the progression from conventional flow cytometry to full spectrum flow cytometry moving as fast as manufacturers create new reagents to expand our fluorochrome palette, a certain factor of flow cytometric analysis continues to appear as a major challenge in data analysis: cellular autofluorescence (AF). More specifically, heterogeneity of cellular AF. The idea of AF in our cytometry assays is not new, one must only search the term “autofluorescence” in this journal to see nearly 1000 publications associated with the subject dating back to the earliest days of publication (<span>1</span>). However, the way we manage and interact with AF in our analysis is evolving at pace with technological advancements and our experimental demands.</p><p>AF is any light emitted from cells by endogenous cellular components that fluoresce. Components like collagen, elastin, tryptophan, NADH, and flavins to name just a few (<span>2</span>), the emission of these components falls predominantly between 400 and 600 nm in mammalian cells. These components, and many others, contribute to the variety of cellular AF found within samples. Cell type, size, granularity, and metabolic state all contribute to variations in AF (<span>2, 3</span>).</p><p>Historically, when encountering a sample with high AF, such as that from an enzymatically digested tissue, one would simply choose red and far-red emitting fluorochromes, thus avoiding the shorter wavelengths most impacted by autofluorescence. In addition, voltages of detectors were often decreased to lower the visual impact of the AF, but this method also dampens the sensitivity of the detector with respect to the intended fluorochrome for analysis. With conventional cytometers and 6–8 parameter assays, this strategy was somewhat effective, but very limiting. The increasing demand to analyze more parameters from each sample means researchers need to embrace new analysis strategies.</p><p>The burden that AF complexity contributes to our assays is easily recognized within our data, but what benefits can we reap if we take the time to optimize our analysis strategies? Without proper care and consideration, data with incorrectly managed heterogeneous AF can result in masking of poorly expressed tertiary markers (<span>4</span>) and even misclassification of cellular phenotypes when AF is incorrectly identified as fluorochrome signal (<span>5</span>). With these potential complications, it is essential to design panels for samples with heterogenous AF to minimize its impact on marker detection and resolution.</p><p>With a spectral flow cytometer, the unique AF properties of different samples can be characterized and leveraged when designing new panels. By thinking of the spectral signature of the AF as just another fluorochrome and implementing good panel design practices with respect to antigen coexpression, fluorochrome brightness, and fluorochrome similarity (<span>6</span>), marker resolution can be substantially improved","PeriodicalId":11068,"journal":{"name":"Cytometry Part A","volume":"105 8","pages":"563-567"},"PeriodicalIF":2.5,"publicationDate":"2024-07-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/cyto.a.24885","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141562914","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":"Noninvasive detection for bladder cancer: Quantitative interferometric imaging flow cytometry","authors":"Shubin Wei, Cheng Lei","doi":"10.1002/cyto.a.24887","DOIUrl":"10.1002/cyto.a.24887","url":null,"abstract":"<p>Noninvasive detection is crucial for achieving a convenient and painless diagnosis of bladder cancer. In a recent report published in <i>Cytometry Part A</i>, Matan Dudaie and coworkers have successfully employed a combination of quantitative interference imaging flow cytometry and machine learning to achieve a noninvasive, label-free approach for detecting bladder cancer cells in urine samples [<span>1</span>].</p><p>Noninvasive detection of bladder cancer based on urine samples has been a highly challenging problem. So far, the gold standard for clinical diagnosis still relies on invasive methods such as cystoscopy and tissue biopsy [<span>2</span>]. These approaches not only have high costs but also carry a certain risk of infection and other side effects after testing, greatly increasing the burden on patients. As the excreted substance of the bladder, urine is the most valuable detection medium [<span>3</span>]. In clinical practice, urine cytology is sometimes used to screen for bladder cancer cells, but the efficiency of this method is very low. To achieve a fast and noninvasive detection method, people have also tried to use flow cytometry to detect urine samples [<span>4</span>]. However, the scattering parameters of flow cytometry are insufficient to differentiate between bladder cancer and normal cells, and relying on fluorescence intensity poses a heightened risk of false positives. These reasons have hindered the effective development of noninvasive detection methods for bladder cancer. Therefore, the development of noninvasive methods for detecting bladder cancer is crucial for reducing the burden on patients.</p><p>Matan Dudaie and coworkers presented their efforts in developing a novel imaging flow cytometry method for noninvasive detection of bladder cancer in <i>Cytometry Part A</i>. By constructing a quantitative interferometric imaging flow cytometry system, they achieved label-free detection of bladder cancer. Their detection unit consists of microfluidic channels and a quantitative interferometric microscope, and the image processing unit is composed of a convolutional neural network (CNN). The key advantage lies in achieving noninvasive, label-free, highly accurate detection of bladder cancer cells simply by collecting urine samples.</p><p>Imaging flow cytometry, as a novel method for cell analysis, can be considered a fusion of optical microscopy and flow cytometry. It enables high-throughput and high-content cell imaging, thereby enhancing the efficiency of morphology-based cell analysis. Currently, the commonly used imaging flow cytometry technique collects images based on intensity imaging principles, where intensity often represents cell morphology but struggles to convey information about the cellular metabolic state.</p><p>The refractive index, as an intrinsic optical property of cells, can provide information about the cellular metabolic state [<span>5</span>]. Through quantitative phase imaging technology, refra","PeriodicalId":11068,"journal":{"name":"Cytometry Part A","volume":"105 8","pages":"568-569"},"PeriodicalIF":2.5,"publicationDate":"2024-07-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/cyto.a.24887","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141579181","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":"Points2Regions: Fast, interactive clustering of imaging-based spatial transcriptomics data","authors":"Axel Andersson,&nbsp;Andrea Behanova,&nbsp;Christophe Avenel,&nbsp;Jonas Windhager,&nbsp;Filip Malmberg,&nbsp;Carolina Wählby","doi":"10.1002/cyto.a.24884","DOIUrl":"10.1002/cyto.a.24884","url":null,"abstract":"<p>Imaging-based spatial transcriptomics techniques generate data in the form of spatial points belonging to different mRNA classes. A crucial part of analyzing the data involves the identification of regions with similar composition of mRNA classes. These biologically interesting regions can manifest at different spatial scales. For example, the composition of mRNA classes on a cellular scale corresponds to cell types, whereas compositions on a millimeter scale correspond to tissue-level structures. Traditional techniques for identifying such regions often rely on complementary data, such as pre-segmented cells, or lengthy optimization. This limits their applicability to tasks on a particular scale, restricting their capabilities in exploratory analysis. This article introduces “Points2Regions,” a computational tool for identifying regions with similar mRNA compositions. The tool's novelty lies in its rapid feature extraction by rasterizing points (representing mRNAs) onto a pyramidal grid and its efficient clustering using a combination of hierarchical and <span></span><math>\u0000 <mrow>\u0000 <mi>k</mi>\u0000 </mrow></math>-means clustering. This enables fast and efficient region discovery across multiple scales without relying on additional data, making it a valuable resource for exploratory analysis. Points2Regions has demonstrated performance similar to state-of-the-art methods on two simulated datasets, without relying on segmented cells, while being several times faster. Experiments on real-world datasets show that regions identified by Points2Regions are similar to those identified in other studies, confirming that Points2Regions can be used to extract biologically relevant regions. The tool is shared as a Python package integrated into TissUUmaps and a Napari plugin, offering interactive clustering and visualization, significantly enhancing user experience in data exploration.</p>","PeriodicalId":11068,"journal":{"name":"Cytometry Part A","volume":"105 9","pages":"677-687"},"PeriodicalIF":2.5,"publicationDate":"2024-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/cyto.a.24884","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141491241","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":"Autofluorescence lifetime flow cytometry with time-correlated single photon counting","authors":"Kayvan Samimi,&nbsp;Ojaswi Pasachhe,&nbsp;Emmanuel Contreras Guzman,&nbsp;Jeremiah Riendeau,&nbsp;Amani A. Gillette,&nbsp;Dan L. Pham,&nbsp;Kasia J. Wiech,&nbsp;Darcie L. Moore,&nbsp;Melissa C. Skala","doi":"10.1002/cyto.a.24883","DOIUrl":"10.1002/cyto.a.24883","url":null,"abstract":"<p>Autofluorescence lifetime imaging microscopy (FLIM) is sensitive to metabolic changes in single cells based on changes in the protein-binding activities of the metabolic co-enzymes NAD(P)H. However, FLIM typically relies on time-correlated single-photon counting (TCSPC) detection electronics on laser-scanning microscopes, which are expensive, low-throughput, and require substantial post-processing time for cell segmentation and analysis. Here, we present a fluorescence lifetime-sensitive flow cytometer that offers the same TCSPC temporal resolution in a flow geometry, with low-cost single-photon excitation sources, a throughput of tens of cells per second, and real-time single-cell analysis. The system uses a 375 nm picosecond-pulsed diode laser operating at 50 MHz, alkali photomultiplier tubes, an FPGA-based time tagger, and can provide real-time phasor-based classification (i.e., gating) of flowing cells. A CMOS camera produces simultaneous brightfield images using far-red illumination. A second PMT provides two-color analysis. Cells are injected into the microfluidic channel using a syringe pump at 2–5 mm/s with nearly 5 ms integration time per cell, resulting in a light dose of 2.65 J/cm² that is well below damage thresholds (25 J/cm² at 375 nm). Our results show that cells remain viable after measurement, and the system is sensitive to autofluorescence lifetime changes in Jurkat T cells with metabolic perturbation (sodium cyanide), quiescent versus activated (CD3/CD28/CD2) primary human T cells, and quiescent versus activated primary adult mouse neural stem cells, consistent with prior studies using multiphoton FLIM. This TCSPC-based autofluorescence lifetime flow cytometer provides a valuable label-free method for real-time analysis of single-cell function and metabolism with higher throughput than laser-scanning microscopy systems.</p>","PeriodicalId":11068,"journal":{"name":"Cytometry Part A","volume":"105 8","pages":"607-620"},"PeriodicalIF":2.5,"publicationDate":"2024-06-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/cyto.a.24883","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141466829","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":"List of Peer-Reviewers for Cytometry Part A (2019 to 2024)","authors":"","doi":"10.1002/cyto.a.24882","DOIUrl":"https://doi.org/10.1002/cyto.a.24882","url":null,"abstract":"","PeriodicalId":11068,"journal":{"name":"Cytometry Part A","volume":"105 6","pages":"420-424"},"PeriodicalIF":3.7,"publicationDate":"2024-06-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141326682","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":"Volume 105A, Number 6, June 2024 Cover Image","authors":"","doi":"10.1002/cyto.a.24752","DOIUrl":"https://doi.org/10.1002/cyto.a.24752","url":null,"abstract":"","PeriodicalId":11068,"journal":{"name":"Cytometry Part A","volume":"105 6","pages":""},"PeriodicalIF":3.7,"publicationDate":"2024-06-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/cyto.a.24752","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141326710","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":"Farewell Cytometry Part A","authors":"Attila Tárnok","doi":"10.1002/cyto.a.24881","DOIUrl":"10.1002/cyto.a.24881","url":null,"abstract":"<p>As I conclude my tenure as Editor-in-Chief of Cytometry Part A, I am pleased to announce that in July 2024, Professor Bartek Rajwa, a long-standing Associate Editor, will assume this role. His extensive experience and dedication make him an excellent successor, poised to lead the journal into an exciting new era. I am confident that under his stewardship, Cytometry Part A will continue to thrive as the premier publication in the field of quantitative single-cell science.</p><p>Reflecting on my journey, I am profoundly grateful for the support of my colleagues at ISAC. I especially want to thank the leadership, conference organizers, and speakers who honored me with a heartfelt farewell at the CYTO 2024 conference in Edinburgh, Scotland. Serving the cytometry community for the past 18 years has been both a privilege and a pleasure. I extend my deepest thanks to our authors and readers, whose innovative contributions and insights have been instrumental in advancing the journal.</p><p>A special note of gratitude goes to the Associate Editors, esteemed experts who have diligently upheld the highest scientific quality standards. Throughout my term, 46 colleagues have supported my efforts, some for the entire duration and others for several years. Additionally, I am indebted to the hundreds of anonymous reviewers whose dedication and critical evaluations have been crucial. The complete list of reviewers from the past 5 years can be found at the end of this issue of Cytometry Part A (add page number).</p><p>This month marks the 44th anniversary of the journal, a milestone that began with its first issue in July 1980, published by founding editor Brian H. Mayall [<span>1</span>]. During my tenure, we celebrated both the 30th [<span>2</span>] and 40th anniversaries [<span>3</span>] of the journal. As I pass the torch to Bartek Rajwa, I wish him a successful start. I am confident that Cytometry Part A, the Journal of Quantitative Single Cell Science, will continue to flourish and make significant contributions to the field for many more years.</p><p><b>Attila Tárnok:</b> Writing – original draft.</p>","PeriodicalId":11068,"journal":{"name":"Cytometry Part A","volume":"105 6","pages":"419"},"PeriodicalIF":3.7,"publicationDate":"2024-06-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/cyto.a.24881","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141317104","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":"CPHEN-017: Comprehensive phenotyping of neutrophil extracellular traps (NETs) on peripheral human neutrophils","authors":"Ceridwyn Jones,&nbsp;Anne La Flamme,&nbsp;Peter Larsen,&nbsp;Kathryn Hally","doi":"10.1002/cyto.a.24851","DOIUrl":"10.1002/cyto.a.24851","url":null,"abstract":"<p>With the recent discovery of their ability to produce neutrophil extracellular traps (NETs), neutrophils are increasingly appreciated as active participants in infection and inflammation. NETs are characterized as large, web-like networks of DNA and proteins extruded from neutrophils, and there is considerable interest in how these structures drive disease in humans. Advancing research in this field is contingent on developing novel tools for quantifying NETosis. To this end, we have developed a 7-marker flow cytometry panel for analyzing NETosis on human peripheral neutrophils following in vitro stimulation, and in fresh circulating neutrophils under inflammatory conditions. This panel was optimized on neutrophils isolated from whole blood and analyzed fresh or in vitro stimulated with phorbol 12-myristate 13-acetate (PMA) or ionomycin, two known NET-inducing agonists. Neutrophils were identified as SSC^highFSC^highCD15⁺CD66b⁺. Neutrophils positive for amine residues and 7-Aminoactinomycin D (7-AAD), our DNA dye of choice, were deemed necrotic (Zombie-NIR⁺7-AAD⁺) and were removed from downstream analysis. Exclusion of Zombie-NIR and positivity for 7-AAD (Zombie-NIR^dim7-AAD⁺) was used here as a marker of neutrophil-appendant DNA, a key feature of NETs. The presence of two NET-associated proteins – myeloperoxidase (MPO) and neutrophil elastase (NE) – were utilized to identify neutrophil-appendant NET events (SSC^highFSC^highCD15⁺CD66b⁺Zombie NIR^dim7-AAD⁺MPO⁺NE⁺). We also demonstrate that NETotic neutrophils express citrullinated histone H3 (H3cit), are concentration-dependently induced by in vitro PMA and ionomycin stimulation but are disassembled with DNase treatment, and are present in both chronic and acute inflammation. This 7-color flow cytometry panel provides a novel tool for examining NETosis in humans.</p>","PeriodicalId":11068,"journal":{"name":"Cytometry Part A","volume":"105 8","pages":"639-652"},"PeriodicalIF":2.5,"publicationDate":"2024-06-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/cyto.a.24851","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141310311","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":"Unbiased method for spectral analysis of cells with great diversity of autofluorescence spectra","authors":"Janna E. G. Roet,&nbsp;Aleksandra M. Mikula,&nbsp;Michael de Kok,&nbsp;Cora H. Chadick,&nbsp;Juan J. Garcia Vallejo,&nbsp;Henk P. Roest,&nbsp;Luc J. W. van der Laan,&nbsp;Charlotte M. de Winde,&nbsp;Reina E. Mebius","doi":"10.1002/cyto.a.24856","DOIUrl":"10.1002/cyto.a.24856","url":null,"abstract":"<p>Autofluorescence is an intrinsic feature of cells, caused by the natural emission of light by photo-excitatory molecular content, which can complicate analysis of flow cytometry data. Different cell types have different autofluorescence spectra and, even within one cell type, heterogeneity of autofluorescence spectra can be present, for example, as a consequence of activation status or metabolic changes. By using full spectrum flow cytometry, the emission spectrum of a fluorochrome is captured by a set of photo detectors across a range of wavelengths, creating an unique signature for that fluorochrome. This signature is then used to identify, or unmix, that fluorochrome's unique spectrum from a multicolor sample containing different fluorescent molecules. Importantly, this means that this technology can also be used to identify intrinsic autofluorescence signal of an unstained sample, which can be used for unmixing purposes and to separate the autofluorescence signal from the fluorophore signals. However, this only works if the sample has a singular, relatively homogeneous and bright autofluorescence spectrum. To analyze samples with heterogeneous autofluorescence spectral profiles, we setup an unbiased workflow to more quickly identify differing autofluorescence spectra present in a sample to include as “autofluorescence signatures” during the unmixing of the full stained samples. First, clusters of cells with similar autofluorescence spectra are identified by unbiased dimensional reduction and clustering of unstained cells. Then, unique autofluorescence clusters are determined and are used to improve the unmixing accuracy of the full stained sample. Independent of the intensity of the autofluorescence and immunophenotyping of cell subsets, this unbiased method allows for the identification of most of the distinct autofluorescence spectra present in a sample, leading to less confounding autofluorescence spillover and spread into extrinsic phenotyping markers. Furthermore, this method is equally useful for spectral analysis of different biological samples, including tissue cell suspensions, peripheral blood mononuclear cells, and in vitro cultures of (primary) cells.</p>","PeriodicalId":11068,"journal":{"name":"Cytometry Part A","volume":"105 8","pages":"595-606"},"PeriodicalIF":2.5,"publicationDate":"2024-06-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/cyto.a.24856","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141305648","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":"Deciphering the aging process through single-cell cytometric technologies","authors":"Lok Ming Tam,&nbsp;Timothy Bushnell","doi":"10.1002/cyto.a.24852","DOIUrl":"10.1002/cyto.a.24852","url":null,"abstract":"<p>The advent of single-cell cytometric technologies, in conjunction with advances in single-cell biology, has significantly propelled forward the field of geroscience, enhancing our comprehension of the mechanisms underlying age-related diseases. Given that aging is a primary risk factor for numerous chronic health conditions, investigating the dynamic changes within the physiological landscape at the granularity of single cells is crucial for elucidating the molecular foundations of biological aging. Utilizing hallmarks of aging as a conceptual framework, we review current literature to delineate the progression of single-cell cytometric techniques and their pivotal applications in the exploration of molecular alterations associated with aging. We next discuss recent advancements in single-cell cytometry in terms of the development in instrument, software, and reagents, highlighting its promising and critical role in driving future breakthrough discoveries in aging research.</p>","PeriodicalId":11068,"journal":{"name":"Cytometry Part A","volume":"105 8","pages":"621-638"},"PeriodicalIF":2.5,"publicationDate":"2024-06-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141283242","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}