多聚赖氨酸在悬浮细胞转染中的应用

目的:悬浮细胞的转染较贴壁细胞存在一定难度,用多聚赖氨酸包被的细胞培养板培养悬浮细胞使其贴壁,用脂质体2000按照贴壁细胞转染的方法转染悬浮细胞,提供一种高效的转染悬浮细胞的方法。方法:悬浮细胞Jurkat或CCRF-CEM培养于包被了0.1 mg/mL多聚赖氨酸的细胞培养板,16 h后洗掉未贴壁的细胞,用脂质体2000分别将pWPXLd质粒或靶向人ABL1基因的小干扰RNA(siRNA)转染细胞,24 h后于荧光显微镜下观察绿色荧光蛋白印迹鉴定siRNA的干扰效率。结果:pWPXLd成功转染2种细胞,siRNA成功抑制了ABL1的表达。结论:质粒和siRNA均能成功转染,提供了一种高效可行的转染悬浮细胞的方法。     

动物细胞培养用多孔载体的研制

多孔载体是一种新型的用于动物细胞培养的优秀的细胞支持物,其内部网状结构的小孔具有固定细胞和保护细胞免受机械损伤的功能,适合于贴壁细胞和悬浮细胞的培养,能提高培养密度,可应用于大规模培养系统。本文综述了多孔载体的物化性质、制作材料和制备方法。     

基于AFM单细胞力谱技术的淋巴瘤细胞黏附力测量（英文）

细胞黏附在细胞生理功能中起着重要的调控作用,对细胞黏附行为进行定量研究有助于理解生命活动内在机制.原子力显微镜(AFM)的出现为研究溶液环境下微纳尺度生物系统的生物物理特性提供了强大工具,特别是AFM单细胞力谱(SCFS)技术可以对单细胞黏附力进行测量.但目前利用SCFS技术进行的研究主要集中在贴壁细胞,对于动物悬浮细胞黏附行为进行的研究还较为缺乏.本文利用AFM单细胞力谱技术(SCFS)对淋巴瘤细胞黏附行为进行了定量测量.研究了淋巴瘤细胞与其单克隆抗体药物利妥昔(利妥昔单抗与淋巴瘤细胞表面的CD20结合后激活免疫攻击)之间的黏附力,分析了利妥昔浓度及SCFS测量参数对黏附力的影响,并对淋巴瘤细胞之间的黏附力进行了测量.实验结果证明了SCFS技术探测动物悬浮细胞黏附行为的能力,加深了对淋巴瘤细胞黏附作用的认识,为单细胞尺度下生物力学探测提供了新的可能.     

基于AFM单细胞力谱技术的淋巴瘤细胞黏附力测量

细胞黏附在细胞生理功能中起着重要的调控作用,对细胞黏附行为进行定量研究有助于理解生命活动内在机制.原子力显微镜（AFM）的出现为研究溶液环境下微纳尺度生物系统的生物物理特性提供了强大工具,特别是AFM单细胞力谱（SCFS）技术可以对单细胞黏附力进行测量.但目前利用SCFS技术进行的研究主要集中在贴壁细胞,对于动物悬浮细胞黏附行为进行的研究还较为缺乏.本文利用AFM单细胞力谱技术（SCFS）对淋巴瘤细胞黏附行为进行了定量测量.研究了淋巴瘤细胞与其单克隆抗体药物利妥昔（利妥昔单抗与淋巴瘤细胞表面的CD20结合后激活免疫攻击）之间的黏附力,分析了利妥昔浓度及SCFS测量参数对黏附力的影响,并对淋巴瘤细胞之间的黏附力进行了测量.实验结果证明了SCFS技术探测动物悬浮细胞黏附行为的能力,加深了对淋巴瘤细胞黏附作用的认识,为单细胞尺度下生物力学探测提供了新的可能.     

动物细胞培养用多孔载体的研制

多孔载体是一种新型的用于动物细胞培养的优秀细胞支持物,其内部网状结构的小孔具有固定细胞和保护细胞免疫机损伤的功能,适合于贴壁细胞和悬浮细胞的培养,能提高培养密度,可应用于大规模培养系统。本文综述了多孔载体的物化性质、制作材料和制备方法。     

体外人表皮细胞对胰蛋白酶耐受的探讨

目的:观察人表皮细胞对胰酶消化的耐受能力,通过不同细胞恢复贴壁时间不同来探索分离和纯化表皮细胞的新方法,并探讨胰酶耐受细胞的干性表达,及其与muse细胞的可能相关性。方法:中性蛋白酶及胰酶消化获取表皮细胞,用0.25%的胰酶悬浮表皮细胞,以3.0×105/m L的细胞密度种植于12孔板,每间隔半小时或1小时终止胰酶1孔,其中胰酶作用时间最长达46小时。记录不同细胞贴壁时间、生长状态,并在接种后第7天,对贴壁细胞进行Nestin、Sox10抗体免疫细胞化学染色。结果:随着胰酶作用时间的延长,贴壁细胞数目递减,细胞贴壁所用时间也延长。所有孔中最早出现的贴壁细胞为树突状细胞,这些细胞开始生长缓慢,大约4天后生长迅速,10天后部分孔出现鱼群样细胞团。部分孔经Nestin、SOX10抗体的免疫细胞化学染色结果均为阳性,其中以Nestin抗体较明显。结论:人表皮细胞对胰酶消化的耐受能达46小时,从形态学观察判断,黑素细胞贴壁早于角质形成细胞,大多数贴壁后细胞增殖力强,干细胞表面标记显示部分阳性。     

贡蕉胚性悬浮细胞原生质体分离的研究

目的:研究不同方法对贡蕉胚性悬浮细胞原生质体分离的影响,筛选适合用于贡蕉胚性悬浮细胞原生质体分离的方案.方法:用不同的酶浓 度、酶组合及不同的酶解时间对贡蕉胚性悬浮细胞进行原生质体分离,并对不同继代时间的胚性悬浮细胞的原生质体产量和活力进行研究.结果:贡蕉胚性悬浮细胞 在酶组合为3.5%纤维素酶R-10、1%离析酶R-10和0.15%果胶酶Y-23的酶溶液中,酶解8h可获 得高产量的原生质体,采用继代7d的贡蕉胚性悬浮细胞进行原生质体分离时获得的原生质体产量最高,达到1.2×107个/mL PCV ECS,原生质体活力达到85%以上.结论: 合适的酶组合、酶浓度和酶解时间有利于贡蕉胚性悬浮细胞的原生质体分离,继代7d 后的贡蕉胚性悬浮细胞最适合用于原生质体分离.     

“空气中悬浮颗粒物的测定与分析”实验的改进

在"双流县垃圾处理现状与展望"的综合实践活动中,学生通过调查走访,通过测定并分析垃圾焚烧发电厂周围空气中悬浮颗粒物的数量,探究空气质量是否受到垃圾焚烧的影响。学生在教材活动方法的基础上,分别改进了收集和观察悬浮颗粒物的工具及计数方法,测量并统计了垃圾焚烧发电厂周围和校园中的空气悬浮颗粒物数量,通过对比2个环境的实验数据,得出了科学可信的实验结果。     

WAVETM反应器和搅拌瓶中微载体球转球放大培养的比较

目的:哺乳动物细胞目前已广泛用于生物工程药物如单抗和疫苗的生产.而用于贴壁细胞规模化培养的微载体,也应时应需得以开发并应用于生物制药.贴壁细胞微载体培养在搅拌罐和WAVETM反应器中都能进行.而如要进行进一步的放大培养,球转球工艺不可或缺.为了发展球转球这一新的放大技术,以及考量WAVETM反应器这种新型大规模培养设备的应用性,大量的细胞培养和球转球实验在WAVETM反应器和搅拌瓶中进行.收集到的数据得以分析比较.方法:将Vero细胞分别接入WAVETM反应器和搅拌瓶中用微载体Cytodex 1进行培养.适当补充营养并控制温度、pH等培养条件使细胞增殖.长满微载体的细胞用清洗、消化等球转球工艺的一系列步骤而分离,并放大接种到新的培养体系.球转球工艺的有效性通过记录并统计分析细胞消化分离的回收率,以及细胞重新接种生长的存活力来评估.结果:统计学分析比较WAVETM反应器和搅拌瓶中得到的细胞分离回收率分别是67.56％和39.39％,数理统计P值小于0.0003;细胞重新接种存活率分别是95.17％和78.45％,P值等于0.0107.结论:在WAVETM反应器中进行的球转球放大工艺,其总体表现和有效性远高于在搅拌瓶中得到的结果.在WAVETM反应器中培养的Vero细胞有很好的细胞状态,作为种子链和生产用罐相比搅拌型反应罐均有很大的优越性.     

利用偏振光散射方法检测癌细胞

血液中病变细胞的检测是疾病诊断的重要依据.在癌症的诊断过程中,血液中存在癌变细胞说明身体已经有癌变组织.因此,识别和检测这些细胞在医学上具有重要的意义.偏振是光的固有属性,可以通过检测光与物质相互作用后的偏振变化来检测物质的性质.本文首次利用偏振光散射方法对悬浮在磷酸缓冲液中的癌细胞进行研究,利用红细胞和癌细胞以及活着的癌细胞和死亡的癌细胞在偏振特征上的不同,对其进行了成功的分辨.该方法具有非侵入、无损伤、高灵敏、高分辨的特点.为癌症诊断和治疗效果评估提供新思路.     

Arginine-mediated dissociation of single cells and cell sheets from a polystyrene culture dish

ABSTRACT

Here, we report a novel non-enzymatic cell dissociation method, based on our finding that adherent cells dissociate rapidly from the polystyrene culture dish when incubated in an l- or d-arginine-containing solution. We also demonstrate the successful detachment of confluent NIH/3T3 cell monolayers from the culture dish as a cell sheet by the addition of an arginine solution.     

Machine learning‐based automated fungal cell counting under a complicated background with ilastik and ImageJ

Measuring the concentration and viability of fungal cells is an important and fundamental procedure in scientific research and industrial fermentation. In consideration of the drawbacks of manual cell counting, large quantities of fungal cells require methods that provide easy, objective and reproducible high‐throughput calculations, especially for samples in complicated backgrounds. To answer this challenge, we explored and developed an easy‐to‐use fungal cell counting pipeline that combined the machine learning‐based ilastik tool with the freeware ImageJ, as well as a conventional photomicroscope. Briefly, learning from labels provided by the user, ilastik performs segmentation and classification automatically in batch processing mode and thus discriminates fungal cells from complex backgrounds. The files processed through ilastik can be recognized by ImageJ, which can compute the numeric results with the macro ‘Fungal Cell Counter’. Taking the yeast Cryptococccus deneoformans and the filamentous fungus Pestalotiopsis microspora as examples, we observed that the customizable software algorithm reduced inter‐operator errors significantly and achieved accurate and objective results, while manual counting with a haemocytometer exhibited some errors between repeats and required more time. In summary, a convenient, rapid, reproducible and extremely low‐cost method to count yeast cells and fungal spores is described here, which can be applied to multiple kinds of eucaryotic microorganisms in genetics, cell biology and industrial fermentation.     

SD大鼠乳鼠皮层神经元细胞原代培养模型的建立及鉴定

摘要 目的：探讨SD大鼠乳鼠皮层神经元细胞原代培养方法,并鉴定其培养效果,以期建立一种生物学功能良好的体外细胞实验模型。方法：取出生24 h的SD大鼠乳鼠,分离出大脑皮层,在胰酶消化之前先进行离心,然后将胰酶消化后多次离心得到的细胞悬液接种于L-多聚赖氨酸包被的培养皿和共聚焦皿中,以加B27的Neurobasal-A培养基进行神经元细胞的原代培养,倒置显微镜下观察培养细胞的生长状态;通过免疫荧光组化的方法采用神经元标记物MAP-2进行神经元纯度的鉴定;在导入Fluo4-AM的原代神经元细胞,观察电刺激后胞内钙离子信号的变化,以验证神经元细胞的生理状态。结果：采用此方法培养的神经元细胞紧密贴壁、分散均匀、状态良好,神经元细胞周围突起相互连接形成网络;经MAP-2免疫荧光组化技术鉴定神经元的纯度达到95%以上;胞内钙离子信号的变化提示所培养的神经元具有良好的生物学功能。结论：该方法能获得纯度较高并且生物学功能良好的原代培养的SD大鼠乳鼠皮层神经元细胞。     

GVC-Net: Global Vascular Context Network for Cerebrovascular Segmentation Using Sparse Labels

ObjectivesCerebrovascular disease is a serious threat to human health. Because of its high mortality and disability rate, early diagnosis and prevention are very important. The performance of existing cerebrovascular segmentation methods based on deep learning depends on the integrity of labels. However, manual labels are usually of low quality and poor connectivity at small blood vessels, which directly affects the cerebrovascular segmentation results.Material and methodIn this paper, we propose a new segmentation network to segment cerebral vessels from MRA images by using sparse labels. The long-distance dependence between vascular structures is captured by the global vascular context module, and the topology is constrained by the hybrid loss function to segment the cerebral vessels with good connectivity.ResultExperiments show that our method performed with a sensitivity, precision, dice similarity coefficient, intersection over union and centerline dice similarity coefficient of 61.24%, 75.58%, 67.66%, 51.13% and 83.79% respectively.ConclusionThe obtained results reveal that the proposed cerebrovascular segmentation network has better segmentation performance for cerebrovascular segmentation under sparse labels, and can suppress the noise of background to a certain extent.     

Clinicopathological and immunohistochemical study of 29 cases of solid-pseudopapillary neoplasms of the pancreas in patients under 20 years of age along with detailed review of literature

Background

Multiplex immunohistochemistry (mIHC) permits the labeling of six or more distinct cell types within a single histologic tissue section. The classification of each cell type requires detection of uniquely colored chromogens localized to cells expressing biomarkers of interest. The most comprehensive and reproducible method to evaluate such slides is to employ digital pathology and image analysis pipelines to whole-slide images (WSIs). Our suite of deep learning tools quantitatively evaluates the expression of six biomarkers in mIHC WSIs. These methods address the current lack of readily available methods to evaluate more than four biomarkers and circumvent the need for specialized instrumentation to spectrally separate different colors. The use case application for our methods is a study that investigates tumor immune interactions in pancreatic ductal adenocarcinoma (PDAC) with a customized mIHC panel.

Methods

Six different colored chromogens were utilized to label T-cells (CD3, CD4, CD8), B-cells (CD20), macrophages (CD16), and tumor cells (K17) in formalin-fixed paraffin-embedded (FFPE) PDAC tissue sections. We leveraged pathologist annotations to develop complementary deep learning-based methods: (1) ColorAE is a deep autoencoder which segments stained objects based on color; (2) U-Net is a convolutional neural network (CNN) trained to segment cells based on color, texture and shape; and (3) ensemble methods that employ both ColorAE and U-Net, collectively referred to as ColorAE:U-Net. We assessed the performance of our methods using: structural similarity and DICE score to evaluate segmentation results of ColorAE against traditional color deconvolution; F1 score, sensitivity, positive predictive value, and DICE score to evaluate the predictions from ColorAE, U-Net, and ColorAE:U-Net ensemble methods against pathologist-generated ground truth. We then used prediction results for spatial analysis (nearest neighbor).

Results

We observed that (1) the performance of ColorAE is comparable to traditional color deconvolution for single-stain IHC images (note: traditional color deconvolution cannot be used for mIHC); (2) ColorAE and U-Net are complementary methods that detect six different classes of cells with comparable performance; (3) combinations of ColorAE and U-Net in ensemble methods outperform ColorAE and U-Net alone; and (4) ColorAE:U-Net ensemble methods can be employed for detailed analysis of the tumor microenvironment (TME).

Summary

We developed a suite of scalable deep learning methods to analyze 6 distinctly labeled cell populations in mIHC WSIs. We evaluated our methods and found that they reliably detected and classified cells in the PDAC tumor microenvironment. We also utilized the ColorAE:U-Net ensemble method to analyze 3 mIHC WSIs with nearest neighbor spatial analysis. We demonstrate a proof of concept that these methods can be employed to quantitatively describe the spatial distribution of immune cells within the tumor microenvironment. These complementary deep learning methods are readily deployable for use in clinical research studies.

     

Biological Image Segmentation Using Region-Scalable Fitting Energy with B-Spline Level Set Implementation and Watershed

ObjectivesImage segmentation plays an important role in the analysis and understanding of the cellular process. However, this task becomes difficult when there is intensity inhomogeneity between regions, and it is more challenging in the presence of the noise and clustered cells. The goal of the paper is propose an image segmentation framework that tackles the above cited problems.Material and methodsA new method composed of two steps is proposed: First, segment the image using B-spline level set with Region-Scalable Fitting (RSF) active contour model, second apply the Watershed algorithm based on new object markers to refine the segmentation and separate clustered cells. The major contributions of the paper are: 1) Use of a continuous formulation of the level set in the B-spline basis, 2) Develop the energy function and its derivative by introducing the RSF model to deal with intensity inhomogeneity, 3) For the Watershed, propose a relevant choice of markers that considers the cell properties.ResultsExperimental results are performed on widely used synthetic images, in addition to simulated and real biological images, without and with additive noise. They attest the high quality of segmentation of the proposed method in terms of quantitative and qualitative evaluation.ConclusionThe proposed method is able to tackle many difficulties at the same time: overlapped intensities, noise, different cell sizes and clustered cells. It provides an efficient tool for image segmentation especially biological ones.     

AI for COVID-19 Detection from Radiographs: Incisive Analysis of State of the Art Techniques,Key Challenges and Future Directions

Background and objectiveIn recent years, Artificial Intelligence has had an evident impact on the way research addresses challenges in different domains. It has proven to be a huge asset, especially in the medical field, allowing for time-efficient and reliable solutions. This research aims to spotlight the impact of deep learning and machine learning models in the detection of COVID-19 from medical images. This is achieved by conducting a review of the state-of-the-art approaches proposed by the recent works in this field.MethodsThe main focus of this study is the recent developments of classification and segmentation approaches to image-based COVID-19 detection. The study reviews 140 research papers published in different academic research databases. These papers have been screened and filtered based on specified criteria, to acquire insights prudent to image-based COVID-19 detection.ResultsThe methods discussed in this review include different types of imaging modality, predominantly X-rays and CT scans. These modalities are used for classification and segmentation tasks as well. This review seeks to categorize and discuss the different deep learning and machine learning architectures employed for these tasks, based on the imaging modality utilized. It also hints at other possible deep learning and machine learning architectures that can be proposed for better results towards COVID-19 detection. Along with that, a detailed overview of the emerging trends and breakthroughs in Artificial Intelligence-based COVID-19 detection has been discussed as well.ConclusionThis work concludes by stipulating the technical and non-technical challenges faced by researchers and illustrates the advantages of image-based COVID-19 detection with Artificial Intelligence techniques.     

A pipeline to quantify spinal cord atrophy with deep learning: Application to differentiation of MS and NMOSD patients

PurposeQuantitative measurement of various anatomical regions of the brain and spinal cord (SC) in MRI images are used as unique biomarkers to consider progress and effects of demyelinating diseases of the central nervous system. This paper presents a fully-automated image processing pipeline which quantifies the SC volume of MRI images.MethodsIn the proposed pipeline, after conducting some pre-processing tasks, a deep convolutional network is utilized to segment the spinal cord cross-sectional area (SCCSA) of each slice. After full segmentation, certain extra slices interpolate between each two adjacent slices using the shape-based interpolation method. Then, a 3D model of the SC is reconstructed, and, by counting the voxels of it, the SC volume is calculated. The performance of the proposed method for the SCCSA segmentation is evaluated on 140 MRI images. Subsequently, to demonstrate the application of the proposed pipeline, we study the differentiations of SC atrophy between 38 Multiple Sclerosis (MS) and 25 Neuromyelitis Optica Spectrum Disorder (NMOSD) patients.ResultsThe experimental results of the SCCSA segmentation indicate that the proposed method, adapted by Mask R-CNN, presented the most satisfactory result with the average Dice coefficient of 0.96. For this method, statistical metrics including sensitivity, specificity, accuracy, and precision are 97.51%, 99.98%, 99.92%, and 98.04% respectively. Moreover, the t-test result (p-value = 0.00089) verified a significant difference between the SC atrophy of MS and NMOSD patients.ConclusionThe pipeline efficiently quantifies the SC volume of MRI images and can be utilized as an affordable computer-aided tool for diagnostic purposes.     

基于注意力机制的RNA碱基关联图预测方法

目的 长链非编码RNA在遗传、代谢和基因表达调控等方面发挥着重要作用。然而，传统的实验方法解析RNA的三级结构耗时长、费用高且操作要求高。此外，通过计算方法来预测RNA的三级结构在近十年来无突破性进展。因此，需要提出新的预测算法来准确的预测RNA的三级结构。所以，本文发展可以用于提高RNA三级结构预测准确性的碱基关联图预测方法。方法 为了利用RNA理化特征信息，本文应用多层全卷积神经网络和循环神经网络的深度学习算法来预测RNA碱基间的接触概率，并通过注意力机制处理RNA序列中碱基间相互依赖的特征。结果 通过多层神经网络与注意力机制结合，本文方法能够有效得到RNA特征值中局部和全局的信息，提高了模型的鲁棒性和泛化能力。检验计算表明，所提出模型对序列长度L的4种标准（L/10、L/5、L/2、L）碱基关联图的预测准确率分别达到0.84、0.82、0.82和0.75。结论 基于注意力机制的深度学习预测算法能够提高RNA碱基关联图预测的准确率，从而帮助RNA三级结构的预测。     

Using federated data sources and Varian Learning Portal framework to train a neural network model for automatic organ segmentation

PurposeIn this study we trained a deep neural network model for female pelvis organ segmentation using data from several sites without any personal data sharing. The goal was to assess its prediction power compared with the model trained in a centralized manner.MethodsVarian Learning Portal (VLP) is a distributed machine learning (ML) infrastructure enabling privacy-preserving research across hospitals from different regions or countries, within the framework of a trusted consortium. Such a framework is relevant in the case when there is a high level of trust among the participating sites, but there are legal restrictions which do not allow the actual data sharing between them. We trained an organ segmentation model for the female pelvic region using the synchronous data distributed framework provided by the VLP.ResultsThe prediction performance of the model trained using the federated framework offered by VLP was on the same level as the performance of the model trained in a centralized manner where all training data was pulled together in one centre.ConclusionsVLP infrastructure can be used for GPU-based training of a deep neural network for organ segmentation for the female pelvic region. This organ segmentation instance is particularly difficult due to the high variation in the organs’ shape and size. Being able to train the model using data from several clinics can help, for instance, by exposing the model to a larger range of data variations. VLP framework enables such a distributed training approach without sharing protected health information.