我们感谢 Hotchkiss 脑研究所高级显微镜平台提供的成像基础设施和专业知识。RWJ 得到了卡尔加里大学 Eyes High 计划的博士后奖学金资助以及加拿大多发性硬化症协会和加拿大罗氏无限制教育奖学金的支持。VWY 获得了加拿大研究主席 Tier 1 计划的薪资支持。这项工作得到了加拿大卫生研究院研究资助 1049959、加拿大多发性硬化症协会资助 3236 和美国国防部国会指导的多发性硬化症研究计划的运营资金的支持。 图 1 是使用 BioRender.com 创建的。本出版物中改编的数字最初发表在 The Journal of Immunology 上。Rajiv W. Jain、David A. Elliott 和 V. Wee Yong。2023. 使用 Histoflow Cytometry 对高参数组织学图像进行单细胞分析。 免疫学杂志。210: 2038-2049.版权所有 © [2023]。美国免疫学家协会

用于高参数组织成像的免疫荧光显微镜检查和分析

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这里描述了组织流式细胞术的使用，这种技术之前已经验证了18.结果表明，当使用光谱重叠染料对组织切片进行染色时，可以使用光谱补偿去除跨通道的渗漏，从而比通常通过传统方法清晰分辨更多的荧光参数。由于使用传统方法难以分析高参数组织学图像，因此提供了一个分析管道来从这些图像中分割单个细胞。通过导出这些单个细胞的荧光特征，证明像在流式细胞术中进行?...

由免疫系统细胞和神经胶质细胞驱动的炎症会导致 CNS 的慢性疾病，其中每个群体都可以促进其他群体的活动 1,2,3。了解免疫系统如何与 CNS 的这些元素相互作用以促进 CNS 炎症是目前感兴趣的主要话题，并且单细胞 RNA 测序等高参数技术极大地促进了这一主题。通过单细胞 RNA 测序，我们发现在几种 CNS 疾病中，神经胶质细胞和免疫系统之间存在广泛的通信 4,5,6。了解这些相互作用如何影响这些疾病对于阐明这些疾病的生物学至关重要。
单细胞测序分析的一个问题是，这些技术需要破坏组织以获得单细胞或细胞核，从而导致空间信息的完全丢失。了解细胞在组织中的位置对于了解细胞在驱动炎症中的作用至关重要。例如，B 细胞等免疫细胞在神经炎症期间可以集中在 CNS 中;然而，它们很少进入 CNS 薄壁组织，而是集中在 CNS 屏障7。鉴于它们的定位，这些细胞不太可能通过与 CNS 薄壁细胞中的神经胶质细胞发生物理相互作用来促进 CNS 炎症，这表明它们可能与神经胶质细胞发生的任何相互作用都会通过分泌因子发生。此外，中枢神经系统疾病中发生的病理通常具有结构 8,9，因此细胞在组织中的定位可以关键地确定它是积极导致疾病还是旁观者。因此，使用空间定向来评估细胞在病理学中的作用是必不可少的。
研究组织中的细胞通常是通过使用免疫组织化学或免疫荧光显微镜来完成的。这些技术的一个问题是，它们通常只能同时对最多四个参数进行成像。这是这些技术的主要限制，因为我们从流式细胞术和单细胞 RNA 测序分析中了解到，许多细胞群需要两个或多个参数进行鉴定;此外，在查找 Cell Type10 的特定子集时，所需的参数数量通常会增加。因此，使用标准成像技术来研究细胞亚群如何在组织内相互作用是不切实际的。
通过可以保留空间信息的较新的高参数方法，例如空间 RNA 测序11 和成像质谱流式细胞术12，已经部分解决了这个问题。虽然这些技术很有价值，但它们确实存在一些问题，例如没有广泛使用、将 3D 数据简化为二维以及需要相当多的专业知识来执行。另一种称为顺序染色的技术，其中组织用一组抗体染色，然后灭活前一组抗体，然后再用另一组抗体染色，无需专用设备或专业知识即可实现高参数组织学 13,14,15.然而，顺序染色可能非常耗费人力，并且需要大量的显微镜检查时间，这对于没有个人显微镜的实验室来说可能不切实际。因此，需要能够扩大荧光参数数量的技术，这些参数可以在广泛可用且及时的显微镜上一次成像。
一旦采集了高参数数据，另一个问题就出现了：传统的图像分析方法不太可能成功分析数据。手动计数或阈值等技术仅在分析由单个参数组成或多个标记具有相同的定位（仅计数重叠信号）时才可行。此限制使传统分析不足以处理高参数数据集。通过从组织学图像中分割单个细胞，然后进行类似流式细胞术的门控策略来识别细胞类型，可以成功分析这些数据集16,17。然而，影响这些分析的另一个问题是，它们仅适用于所有感兴趣的细胞都彼此物理分离的数据集，因为这些技术没有采用可以准确分离物理接触细胞的方法。因此，需要一种更新的方法，即使细胞处于物理接触状态，也可以对组织学切片进行单细胞分析。
在本文中，描述了一种称为组织流式细胞术的简单方案，该方案之前已经介绍过18 ，它扩展了可以使用广泛使用的显微镜同时成像的荧光参数的数量。该方案的工作原理是用光谱重叠染料对组织进行染色，然后使用光谱补偿去除重叠通道的渗出，以获得清晰的单一染色。为了便于分析高参数组织学图像，描述了一个详细的分析管道，该管道从组织切片中提取单个细胞，以便使用类似流式细胞术的门控策略将细胞分选为不同的群体。该方案适用于细胞弥散存在的组织和细胞紧密压实在一起的组织，使该技术可用于研究稳态和神经炎症中的 CNS 等组织。因此，组织流式细胞术是研究复杂细胞类型之间相互作用的有用技术，这些细胞类型需要多个细胞标志物来定义细胞，同时保持空间信息。

<table><tbody><tr><td>100% Ethanol</td><td>Sigma</td><td>676829-1L</td><td></td></tr><tr><td>4% PFA</td><td>Electron Microscopy Sciences</td><td>157-4</td><td></td></tr><tr><td>Anaconda</td><td>N/A</td><td>N/A</td><td>https://www.anaconda.com/download</td></tr><tr><td>Bovine Serum Albumin</td><td>Sigma</td><td>A4503-50G</td><td></td></tr><tr><td>Cold fish stain gelatin&amp;nbsp;</td><td>Sigma</td><td>G7765</td><td></td></tr><tr><td>Collating multichannel data from Imaris.ipynb script</td><td>N/A</td><td>N/A</td><td>https://github.com/elliottcalgary/Histoflow-Cytometry-Analysis-</td></tr><tr><td>Convert FlowJo output to txt file for Cell selection in Imaris.ipynb script</td><td>N/A</td><td>N/A</td><td>https://github.com/elliottcalgary/Histoflow-Cytometry-Analysis-</td></tr><tr><td>Donkey anti-rat Alexa Fluor 647</td><td>JacksonImmunoResearch</td><td>712-605-153</td><td>1:300 concentration</td></tr><tr><td>Donkey anti-rat DyLight 405</td><td>Jackson ImmunoResearch</td><td>712-475-153</td><td>1:200 concentration</td></tr><tr><td>Donkey Serum</td><td>JacksonImmunoResearch</td><td>017-000-001</td><td></td></tr><tr><td>F(ab&#039;)2-Goat anti-Mouse IgG PerCP-eFluor 710</td><td>Thermofisher</td><td>46-4010-82</td><td>1:25 concentration</td></tr><tr><td>FIJI</td><td>N/A</td><td>N/A</td><td>https://imagej.net/software/fiji/</td></tr><tr><td>FlowJo</td><td>FlowJo LLC</td><td></td><td>Software 4</td></tr><tr><td>Fluorescence spectraviewer</td><td></td><td></td><td>https://www.thermofisher.com/order/fluorescence-spectraviewer/#!/</td></tr><tr><td>Fluoromount-G</td><td>Southern Biotech</td><td>0100-01</td><td></td></tr><tr><td>Fresh frozen human tonsil sections</td><td>amsbio</td><td>HF-707</td><td></td></tr><tr><td>Glass coverslip</td><td>VWR</td><td>48393 106</td><td></td></tr><tr><td>Goat anti-human IgA Alexa Fluor 488</td><td>JacksonImmunoResearch</td><td>109-546-011</td><td>1:400 concentration</td></tr><tr><td>Goat anti-human IgG Cy3</td><td>JacksonImmunoResearch</td><td>709-166-098</td><td>1:400 concentration</td></tr><tr><td>Goat anti-human IgM Dylight 405</td><td>JacksonImmunoResearch</td><td>109-476-129</td><td>1:300 concentration</td></tr><tr><td>Goat anti-rabbit A546</td><td>Thermo Fisher Scientific</td><td>A-11035</td><td>1:250 concentration</td></tr><tr><td>Goat anti-rabbit IgG PE-Alexa Fluor 610</td><td>Thermofisher</td><td>A-20981</td><td>1:250 concentration</td></tr><tr><td>Horse Serum</td><td>Sigma</td><td>H1138</td><td></td></tr><tr><td>Ilastik</td><td>N/A</td><td>N/A</td><td>https://www.ilastik.org/</td></tr><tr><td>Ilastik FIJI plugin</td><td>N/A</td><td>N/A</td><td>https://www.ilastik.org/documentation/fiji_export/plugin</td></tr><tr><td>Imaris File Converter</td><td>Oxford Instruments</td><td></td><td>Software 2</td></tr><tr><td>Imaris with cell module</td><td>Oxford Instruments</td><td></td><td>Software 3</td></tr><tr><td>kimwipe</td><td>Kimtech</td><td>34155</td><td></td></tr><tr><td>LasX Life Science software</td><td>Leica</td><td></td><td>Software 1</td></tr><tr><td>Mouse anti-human CD20</td><td>VWR</td><td>CA95024-322</td><td>1:40 concentration</td></tr><tr><td>Mouse anti-human CD38 APC-R700</td><td>BD Biosciences</td><td>564980</td><td>1:20 concentration</td></tr><tr><td>Normal Goat Serum</td><td>JacksonImmunoResearch</td><td>005-000-001</td><td></td></tr><tr><td>Normal Mouse Serum</td><td>JacksonImmunoResearch</td><td>015-000-001</td><td></td></tr><tr><td>Normal Rabbit Serum</td><td>JacksonImmunoResearch</td><td>011-000-001</td><td></td></tr><tr><td>Normal Rat Serum</td><td>JacksonImmunoResearch</td><td>012-000-120</td><td></td></tr><tr><td>Nuclear Yellow</td><td>Abcam</td><td>ab138903</td><td>Dissolve in DMSO at a concentration of 2 mg/ml and store at 4&amp;deg;C in the dark</td></tr><tr><td>PAP pen</td><td>Cedarlane</td><td>MU22</td><td></td></tr><tr><td>PBS</td><td>Gibco</td><td>10010-023</td><td></td></tr><tr><td>Rabbit anti-human Ki67</td><td>Abcam</td><td>ab15580</td><td>1:500 concentration</td></tr><tr><td>Rabbit anti-mouse Iba1</td><td>Wako</td><td>019-19741</td><td>1:500 concentration</td></tr><tr><td>Rat anti-human Blimp1</td><td>Thermofisher</td><td>14-5963-82</td><td>1:40 concentration</td></tr><tr><td>Rat anti-mouse B220 Alexa Fluor 647</td><td>BioLegend</td><td>103226</td><td>1:250 concentration</td></tr><tr><td>Rat anti-mouse CD138</td><td>Biolegend</td><td>142502</td><td>1:200 concentration</td></tr><tr><td>Rat anti-mouse CD3 PE-eFluor 610</td><td>Thermo Fisher Scientific</td><td>61-0032-82</td><td>1:40 concentration</td></tr><tr><td>Rat anti-mouse CD4 Alexa Fluor 488</td><td>BioLegend</td><td>100529</td><td>1:200 concentration</td></tr><tr><td>Rat anti-mouse CD45 allophycocyanin-R700</td><td>BD Biosciences</td><td>565478</td><td>1:50 concentration</td></tr><tr><td>Rat anti-mouse IgD PerCP-eFluor 710</td><td>Thermo Fisher Scientific</td><td>46-5993-82</td><td>1:50 concentration</td></tr><tr><td>SP8 Confocal microscope</td><td>Leica</td><td></td><td></td></tr><tr><td>Triton X-100</td><td>Sigma</td><td>X100-500ml</td><td></td></tr><tr><td>Trueblack</td><td>Biotium</td><td>23007</td><td></td></tr><tr><td>Tween-20</td><td>Sigma</td><td>P7949-500ml</td><td></td></tr><tr><td>Ultracomp ebeads</td><td>Thermofisher</td><td>01-2222-42</td><td></td></tr></tbody></table>

generating and analyzing high-parameter histology images with histoflow cytometry

使用组织学来研究组织切片（例如来自中枢神经系统 （CNS） 的组织切片）中的免疫细胞多样性受到一次可以成像的荧光参数数量的严重限制。大多数免疫细胞亚群是使用流式细胞术通过使用蛋白质标志物的复杂组合来定义的，通常需要四个或更多参数才能最终识别，这超出了大多数传统显微镜的能力。由于流式细胞术会解离组织并丢失空间信息，因此需要能够在询问复杂细胞类型的作用的同时保留空间信息的技术。这些问题在这里通过创建一种方法来解决，该方法通过收集光谱重叠荧光团的信号并使用光谱解混来分离每个单独荧光团的信号来扩展可以成像的荧光参数的数量。然后使用分析管道处理这些图像，以拍摄高参数组织学图像并从这些图像中提取单细胞，以便可以在单细胞水平上分析每个细胞的独特荧光特性。然后，使用类似流式细胞术的设门策略，可以将细胞分析成亚群并定位回组织学切片，不仅可以量化它们的丰度，还可以确定它们如何与组织环境相互作用。总体而言，证明了使用组织流式细胞术在组织学切片中研究复杂免疫群体的简单性和潜力。

<img alt="Figure 1" class="xfigimg" src="/files/ftp_upload/66889/66889fig01.jpg"> 图 1：组织流式细胞术工作流程。 组织切片用光谱重叠染料染色（步骤 1）。使用与可调谐带通滤光片配对的单个激发激光器收集图像，以最大限度地减少荧光团之间的光谱渗漏（步骤 2）。通道之间的光谱渗出根据使用单一颜色染色组织对照生成的补偿矩阵（步骤 3...

作者聚焦：用于神经科学研究的增强型多重免疫荧光显微镜实验方案

这里描述的是一种可用于通过免疫荧光显微镜对五个或更多荧光参数进行成像的方法。概述了从这些图像中提取单细胞并通过类似流式细胞术的门控策略进行单细胞分析的分析管道，该管道可以识别组织切片中的细胞亚群。

使用 Histoflow 流式细胞术生成和分析高参数组织学图像

The usage of histology to investigate immune cell diversity in tissue sections such as those derived from the central nervous system (CNS) is critically ...

我们的首要目标是研究免疫系统细胞和中枢神经系统内的神经胶质细胞如何促进多发性硬化症等神经系统疾病的发展和进展。免疫荧光显微镜检查是生物学科中广泛使用的标准技术，对神经科学和免疫学研究至关重要。困扰研究人员的主要问题之一是，由于传统显微镜的限制，免疫荧光显微镜通常受限于一次可以成像的探针数量。在该协议中，我们描述了一种扩展许多显微镜一次可以成像的探针数量的方法，并提供了一个分析管道来处理信息密集的图像。该协议的优点是它可以适应许多广泛使用的显微镜并且易于执行，这将使更多的实验室可以使用多重组织学分析。

generating-analyzing-high-parameter-histology-images-with-histoflow

Research

JoVE Journal

Neuroscience

Generating and Analyzing High-Parameter Histology Images with Histoflow Cytometry

281 次观看.  University of Calgary. 我们的首要目标是研究免疫系统细胞和中枢神经系统内的神经胶质细胞如何促进多发性硬化症等神经系统疾病的发展和进展。免疫荧光显微镜检查是生物学科中广泛使用的标准技术，对神经科学和免疫学研究至关重要。困扰研究人员的主要问题之一是，由于传统显微镜的限制，免疫荧光显微镜通常受限于一次可以成像的探针数量。在该协议中，我们描述了一种扩展许多?...

视频: 作者聚焦：用于神经科学研究的增强型多重免疫荧光显微镜实验方案

Automated Quantification of Hematopoietic Cell &#8211; Stromal Cell Interactions in Histological Images of Undecalcified Bone

Confocal microscopy is the method of choice for the analysis of localization of multiple cell types within complex tissues such as the bone marrow. However, the analysis and quantification of cellular localization is difficult, as in many cases it relies on manual counting, thus bearing the risk of introducing a rater-dependent bias and reducing interrater reliability. Moreover, it is often difficult to judge whether the co-localization between two cells results from random positioning, especially when cell types differ strongly in the frequency of their occurrence. Here, a method for unbiased quantification of cellular co-localization in the bone marrow is introduced. The protocol describes the sample preparation used to obtain histological sections of whole murine long bones including the bone marrow, as well as the staining protocol and the acquisition of high-resolution images. An analysis workflow spanning from the recognition of hematopoietic and non-hematopoietic cell types in 2-dimensional (2D) bone marrow images to the quantification of the direct contacts between those cells is presented. This also includes a neighborhood analysis, to obtain information about the cellular microenvironment surrounding a certain cell type. In order to evaluate whether co-localization of two cell types is the mere result of random cell positioning or reflects preferential associations between the cells, a simulation tool which is suitable for testing this hypothesis in the case of hematopoietic as well as stromal cells, is used. This approach is not limited to the bone marrow, and can be extended to other tissues to permit reproducible, quantitative analysis of histological data.

A strategy to quantitatively analyze histological data in the bone marrow is presented. Confocal microscopy of fluorescently labeled cells in tissue sections results in 2-dimensional images, which are automatically analyzed. Co-localization analyses of different cell types are compared to data from simulated images, giving quantitative information about cellular interactions.

造血干细胞的自动定量 - 在脱钙骨的组织学图像间质细胞的相互作用

Confocal microscopy is the method of choice for the analysis of localization of multiple cell types within complex tissues such as the bone marrow. ...

科研

发育生物学

Microfluidic Imaging Flow Cytometry by Asymmetric-detection Time-stretch Optical Microscopy (ATOM)

Scaling the number of measurable parameters, which allows for multidimensional data analysis and thus higher-confidence statistical results, has been the main trend in the advanced development of flow cytometry. Notably, adding high-resolution imaging capabilities allows for the complex morphological analysis of cellular/sub-cellular structures. This is not possible with standard flow cytometers. However, it is valuable for advancing our knowledge of cellular functions and can benefit life science research, clinical diagnostics, and environmental monitoring. Incorporating imaging capabilities into flow cytometry compromises the assay throughput, primarily due to the limitations on speed and sensitivity in the camera technologies. To overcome this speed or throughput challenge facing imaging flow cytometry while preserving the image quality, asymmetric-detection time-stretch optical microscopy (ATOM) has been demonstrated to enable high-contrast, single-cell imaging with sub-cellular resolution, at an imaging throughput as high as 100,000 cells/s. Based on the imaging concept of conventional time-stretch imaging, which relies on all-optical image encoding and retrieval through the use of ultrafast broadband laser pulses, ATOM further advances imaging performance by enhancing the image contrast of unlabeled/unstained cells. This is achieved by accessing the phase-gradient information of the cells, which is spectrally encoded into single-shot broadband pulses. Hence, ATOM is particularly advantageous in high-throughput measurements of single-cell morphology and texture &#8211; information indicative of cell types, states, and even functions. Ultimately, this could become a powerful imaging flow cytometry platform for the biophysical phenotyping of cells, complementing the current state-of-the-art biochemical-marker-based cellular assay. This work describes a protocol to establish the key modules of an ATOM system (from optical frontend to data processing and visualization backend), as well as the workflow of imaging flow cytometry based on ATOM, using human cells and micro-algae as the examples.

Microfluidic Imaging Flow Cytometry by Asymmetric-detection Time-stretch Optical Microscopy ATOM

This protocol describes the implementation of an asymmetric-detection time-stretch optical microscopy system for single-cell imaging in ultrafast microfluidic flow and its applications in imaging flow cytometry.

微流控成像流式细胞仪通过不对称检测时间延伸光学显微镜（ATOM）

Scaling the number of measurable parameters, which allows for multidimensional data analysis and thus higher-confidence statistical results, has been the ...

工程学

Combining Fluidic Devices with Microscopy and Flow Cytometry to Study Microbial Transport in Porous Media Across Spatial Scales

Understanding the transport, dispersion and deposition of microorganisms in porous media is a complex scientific task comprising topics as diverse as hydrodynamics, ecology and environmental engineering. Modeling bacterial transport in porous environments at different spatial scales is critical to better predict the consequences of bacterial transport, yet current models often fail to up-scale from laboratory to field conditions. Here, we introduce experimental tools to study bacterial transport in porous media at two spatial scales. The aim of these tools is to obtain macroscopic observables (such as breakthrough curves or deposition profiles) of bacteria injected into transparent porous matrices. At the small scale (10-1000 &#181;m), microfluidic devices are combined with optical video-microscopy and image processing to obtain breakthrough curves and, at the same time, to track individual bacterial cells at the pore scale. At larger scale, flow cytometry is combined with a self-made robotic dispenser to obtain breakthrough curves. We illustrate the utility of these tools to better understand how bacteria are transported in complex porous media such as the hyporheic zone of streams. As these tools provide simultaneous measurements across scales, they pave the way for mechanism-based models, critically important for upscaling. Application of these tools may not only contribute to the development of novel bioremediation applications but also shed new light on the ecological strategies of microorganisms colonizing porous substrates.

Breakthrough curves (BTCs) are efficient tools to study the transport of bacteria in porous media. Here we introduce tools based on fluidic devices in combination with microscopy and flow cytometric counting to obtain BTCs.

将流体设备与显微镜和流式细胞学相结合，研究跨空间尺度的多孔介质中的微生物传输

Understanding the transport, dispersion and deposition of microorganisms in porous media is a complex scientific task comprising topics as diverse as ...

环境科学

Qualitative and Quantitative Analysis of the Immune Synapse in the Human System Using Imaging Flow Cytometry

The immune synapse is the area of communication between T cells and antigen-presenting cells (APCs). T cells polarize surface receptors and proteins towards the immune synapse to assure a stable binding and signal exchange. Classical confocal, TIRF, or super-resolution microscopy have been used to study the immune synapse. Since these methods require manual image acquisition and time-consuming quantification, the imaging of rare events is challenging. Here, we describe a workflow that enables the morphological analysis of tens of thousands of cells. Immune synapses are induced between primary human T cells in pan-leukocyte preparations and Staphylococcus aureus enterotoxin B (SEB)-loaded Raji cells as APCs. Image acquisition is performed with imaging flow cytometry, also called In-Flow microscopy, which combines features of a flow cytometer and a fluorescence microscope. A complete gating strategy for identifying T cell/APC couples and analyzing the immune synapses is provided. As this workflow allows the analysis of immune synapses in unpurified pan-leukocyte preparations and hence requires only a small volume of blood (i.e., 1 mL), it can be applied to samples from patients. Importantly, several samples can be prepared, measured, and analyzed in parallel.

Here, we describe a complete workflow for the qualitative and quantitative analysis of immune synapses between primary human T cells and antigen-presenting cells. The method is based on imaging flow cytometry, which allows the acquisition and evaluation of several thousand cell images within a relatively short period of time.

影像性流式细胞仪对人体免疫突触的定性和定量分析

The immune synapse is the area of communication between T cells and antigen-presenting cells (APCs). T cells polarize surface receptors and proteins ...

免疫与感染

ExCYT: A Graphical User Interface for Streamlining Analysis of High-Dimensional Cytometry Data

With the advent of flow cytometers capable of measuring an increasing number of parameters, scientists continue to develop larger panels to phenotypically explore characteristics of their cellular samples. However, these technological advancements yield high-dimensional data sets that have become increasingly difficult to analyze objectively within traditional manual-based gating programs. In order to better analyze and present data, scientists partner with bioinformaticians with expertise in analyzing high-dimensional data to parse their flow cytometry data. While these methods have been shown to be highly valuable in studying flow cytometry, they have yet to be incorporated in a straightforward and easy-to-use package for scientists who lack computational or programming expertise. To address this need, we have developed ExCYT, a MATLAB-based Graphical User Interface (GUI) that streamlines the analysis of high-dimensional flow cytometry data by implementing commonly employed analytical techniques for high-dimensional data including dimensionality reduction by t-SNE, a variety of automated and manual clustering methods, heatmaps, and novel high-dimensional flow plots. Additionally, ExCYT provides traditional gating options of select populations of interest for further t-SNE and clustering analysis as well as the ability to apply gates directly on t-SNE plots. The software provides the additional advantage of working with either compensated or uncompensated FCS files. In the event that post-acquisition compensation is required, the user can choose to provide the program a directory of single stains and an unstained sample. The program detects positive events in all channels and uses this select data to more objectively calculate the compensation matrix. In summary, ExCYT provides a comprehensive analysis pipeline to take flow cytometry data in the form of FCS files and allow any individual, regardless of computational training, to use the latest algorithmic approaches in understanding their data.

ExCYT is a MATLAB-based Graphical User Interface (GUI) that allows users to analyze their flow cytometry data via commonly employed analytical techniques for high-dimensional data including dimensionality reduction via t-SNE, a variety of automated and manual clustering methods, heatmaps, and novel high-dimensional flow plots.

超 cyt: 一种用于简化高维细胞学数据分析的图形用户界面

With the advent of flow cytometers capable of measuring an increasing number of parameters, scientists continue to develop larger panels to phenotypically ...

Multicolor Flow Cytometry-based Quantification of Mitochondria and Lysosomes in T Cells

T cells utilize different metabolic programs to match their functional needs during differentiation and proliferation. Mitochondria are crucial cellular components responsible for supplying cell energy; however, excess mitochondria also produce reactive oxygen species (ROS) that could cause cell death. Therefore, the number of mitochondria must constantly be adjusted to fit the needs of the cells. This dynamic regulation is achieved in part through the function of lysosomes that remove surplus/damaged organelles and macromolecules. Hence, cellular mitochondrial and lysosomal contents are key indicators to evaluate the metabolic adjustment of cells. With the development of probes for organelles, well-characterized lysosome or mitochondria-specific dyes have become available in various formats to label cellular lysosomes and mitochondria. Multicolor flow cytometry is a common tool to profile cell phenotypes, and has the capability to be integrated with other assays. Here, we present a detailed protocol of how to combine organelle-specific dyes with surface markers staining to measure the amount of lysosomes and mitochondria in different T cell populations on a flow cytometer.

This article illustrates a powerful method to quantify mitochondria or lysosomes in living cells. The combination of lysosome- or mitochondria-specific dyes with fluorescently conjugated antibodies against surface markers allows the quantification of these organelles in mixed cell populations, like primary cells harvested from tissue samples, by using multicolor flow cytometry.

基于多色流式细胞仪的 t 细胞线粒体和溶酶体的定量

T cells utilize different metabolic programs to match their functional needs during differentiation and proliferation. Mitochondria are crucial cellular ...

High-Dimensionality Flow Cytometry for Immune Function Analysis of Dissected Implant Tissues

The success of implanting laboratory-grown tissue or a medical device in an individual is subject to the immune response of the recipient host. Considering an implant as a foreign body, a hostile and dysregulated immune response may result in the rejection of the implant, while a regulated response and regaining of homeostasis can lead to its acceptance. Analyzing the microenvironments of implants dissected out under in vivo or ex vivo&#160;settings can help in understanding the pattern of immune response, which can ultimately help in developing new generations of biomaterials. Flow cytometry is a well-known technique for characterizing immune cells and their subsets based on their cell surface markers. This review describes a protocol based on manual dicing, enzymatic digestion, and filtration through a cell strainer for the isolation of uniform cell suspensions from dissected implant tissue. Further, a multicolor flow cytometry staining protocol has been explained, along with steps for initial cytometer settings to characterize and quantify these isolated cells by flow cytometry.

Isolation of cells from dissected implants and their characterization by flow cytometry can significantly contribute to understanding the pattern of immune response against implants. This paper describes a precise method for the isolation of cells from dissected implants and their staining for flow cytometric analysis.

高维流式细胞术用于解剖种植体组织的免疫功能分析

The success of implanting laboratory-grown tissue or a medical device in an individual is subject to the immune response of the recipient host. ...

生物工程

Visualization and Quantification of High-Dimensional Cytometry Data using Cytofast and the Upstream Clustering Methods FlowSOM and Cytosplore

The complexity of data generated by mass cytometry has necessitated new tools to rapidly visualize analytic outcomes. Clustering methods like Cytosplore or FlowSOM are used for the visualization and identification of cell clusters. For downstream analysis, a newly developed R package, Cytofast, can generate a rapid visualization of results from clustering methods. Cytofast takes into account the phenotypic characterization of cell clusters, calculates the cell cluster abundance, then quantitatively compares groups. This protocol explains the applications of Cytofast to the use of mass cytometry data based on modulation of the immune system in the tumor microenvironment (i.e., the natural killer [NK] cell response) upon tumor challenge followed by immunotherapy (PD-L1 blockade). Demonstration of the usefulness of Cytofast with FlowSOM and Cytosplore is shown. Cytofast rapidly generates visual representations of group-related immune cell clusters and correlations with immune system composition. Differences are observed in the clustering analysis, but separation between groups are visible with both clustering methods. Cytofast visually shows the patterns induced by PD-L1 treatment that include a higher abundance of activated NK cell subsets, expressing a higher intensity of activation markers (i.e., CD54 or CD11c).

Cytofast is a visualization tool used to analyze output from clustering. Cytofast can be used to compare two clustering methods: FlowSOM and Cytosplore. Cytofast can rapidly generate a quantitative and qualitative overview of mass cytometry data and highlight the main differences between different clustering algorithms.

使用Cytofast和上游聚类方法流和细胞溅的高维细胞测量数据的可视化和量化

The complexity of data generated by mass cytometry has necessitated new tools to rapidly visualize analytic outcomes. Clustering methods like Cytosplore ...

Simultaneous Assessment of Kinship, Division Number, and Phenotype via Flow Cytometry for Hematopoietic Stem and Progenitor Cells

Few techniques can assess phenotype and fate for the same cell simultaneously. Most of the current protocols used to characterize phenotype, although able to generate large datasets, necessitate the destruction of the cell of interest, making it impossible to assess its functional fate. Heterogeneous biological differentiating systems like hematopoiesis are therefore difficult to describe. Building on cell division tracking dyes, we further developed a protocol to simultaneously determine kinship, division number, and differentiation status for many single hematopoietic progenitors. This protocol allows the assessment of the ex vivo differentiation potential of murine and human hematopoietic progenitors, isolated from various biological sources. Moreover, as it is based on flow cytometry and a limited number of reagents, it can quickly generate a large amount of data, at the single-cell level, in a relatively inexpensive manner. We also provide the analytical pipeline for single-cell analysis, combined with a robust statistical framework. As this protocol allows the linking of cell division and differentiation at the single-cell level, it can be used to quantitatively assess symmetric and asymmetric fate commitment, the balance between self-renewal and differentiation, and the number of divisions for a given commitment fate. Altogether, this protocol can be used in experimental designs aiming to unravel the biological differences between hematopoietic progenitors, from a single-cell perspective.

Simultaneous Assessment of Kinship, Division Number, and Phenotype via Flow Cytometry for Hematopoietic Stem and Progenitor Cells

Presented here is a flow cytometry-based technique that allows for simultaneously measuring the number of cell divisions, surface cell phenotype, and cellular kinship. Those properties can be tested statistically using a permutation-based framework.

通过流式细胞术 同时 评估造血干细胞和祖细胞的亲缘关系、分裂数和表型

Few techniques can assess phenotype and fate for the same cell simultaneously. Most of the current protocols used to characterize phenotype, although able ...

High-Throughput, Multi-Image Cryohistology of Mineralized Tissues

There is an increasing need for efficient phenotyping and histopathology of a variety of tissues. This phenotyping need is evident with the ambitious projects to disrupt every gene in the mouse genome. The research community needs rapid and inexpensive means to phenotype tissues via histology. Histological analyses of skeletal tissues are often time consuming and semi-quantitative at best, regularly requiring subjective interpretation of slides from trained individuals. Here, we present a cryohistological paradigm for efficient and inexpensive phenotyping of mineralized tissues. First, we present a novel method of tape-stabilized cryosectioning that preserves the morphology of mineralized tissues. These sections are then adhered rigidly to glass slides and imaged repeatedly over several rounds of staining. The resultant images are then aligned either manually or via computer software to yield composite stacks of several layered images. The protocol allows for co-localization of numerous molecular signals to specific cells within a given section. In addition, these fluorescent signals can be quantified objectively via computer software. This protocol overcomes many of the shortcomings associated with histology of mineralized tissues and can serve as a platform for high-throughput, high-content phenotyping of musculoskeletal tissues moving forward.

In this manuscript, we present a high-throughput, semi-automated cryohistology platform to produce aligned composite images of multiple response measures from several rounds of fluorescent imaging on frozen sections of mineralized tissues.

高通量，矿化组织的多图像Cryohistology

There is an increasing need for efficient phenotyping and histopathology of a variety of tissues. This phenotyping need is evident with the ambitious ...

使用 Histoflow 流式细胞术生成和分析高参数组织学图像

副本

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此视频中的章节

造血干细胞的自动定量 - 在脱钙骨的组织学图像间质细胞的相互作用

微流控成像流式细胞仪通过不对称检测时间延伸光学显微镜（ATOM）

将流体设备与显微镜和流式细胞学相结合，研究跨空间尺度的多孔介质中的微生物传输

影像性流式细胞仪对人体免疫突触的定性和定量分析

超 cyt: 一种用于简化高维细胞学数据分析的图形用户界面

基于多色流式细胞仪的 t 细胞线粒体和溶酶体的定量

高维流式细胞术用于解剖种植体组织的免疫功能分析

使用Cytofast和上游聚类方法流和细胞溅的高维细胞测量数据的可视化和量化

通过流式细胞术同时评估造血干细胞和祖细胞的亲缘关系、分裂数和表型

高通量，矿化组织的多图像Cryohistology

使用 Histoflow 流式细胞术生成和分析高参数组织学图像

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此视频中的章节

造血干细胞的自动定量 - 在脱钙骨的组织学图像间质细胞的相互作用

微流控成像流式细胞仪通过不对称检测时间延伸光学显微镜（ATOM）

将流体设备与显微镜和流式细胞学相结合，研究跨空间尺度的多孔介质中的微生物传输

影像性流式细胞仪对人体免疫突触的定性和定量分析

超 cyt: 一种用于简化高维细胞学数据分析的图形用户界面

基于多色流式细胞仪的 t 细胞线粒体和溶酶体的定量

高维流式细胞术用于解剖种植体组织的免疫功能分析

使用Cytofast和上游聚类方法流和细胞溅的高维细胞测量数据的可视化和量化

通过流式细胞术 同时 评估造血干细胞和祖细胞的亲缘关系、分裂数和表型

高通量，矿化组织的多图像Cryohistology

通过流式细胞术同时评估造血干细胞和祖细胞的亲缘关系、分裂数和表型