In this November issue of Cytometry Part A, you will find a collection of publications describing diverse applications of quantitative image cytometry in the investigation of cell populations in tumors, investigation of tumor cell physiology, and quantitation of molecule localization.

Macrophages inside tumors are the often‐neglected players in tumor progression and defense. Whereas in PubMed a search for the key term “tumor‐infiltrating lymphocytes” yields over 11,000 hits, searches for “tumor‐infiltrating” or “tumor‐associated macrophages” retrieve just about 5,000. Arnaud‐Sampaio and colleagues dedicate their review (this issue, 1109–1126) to tumor‐associated macrophages and their impact on tumor destruction or promotion, depending on the purinergic signaling pathways. They use an in vitro model to exemplify the importance of automated image cytometry to understand biological actions of macrophages within tissues. To this end, they employed a commercial image cytometry system to determine cell type and quantify cell microenvironmental relations and to yield quantitative, observer‐independent data (1).

Various keratin types have a central role in cancer cell malignancy. Lam and colleagues (this issue, 1145–1155) combined image cytometry with digital holographic microscopy and cell motility assays to investigate the role of keratin 19, which is highly expressed in several types of cancer cells. Previously, the authors (2) introduced a machine learning approach to digital holographic microscopy for the robust and label‐free identification and discrimination of cancer and noncancer cell lines. In their present work, they focused on the role of keratin 19 in cancer cell shape and motility. They found that knocking out keratin 19 by CRISPR‐technology in a breast cancer cell line resulted in cell shape changes and that cell motility is clearly reduced.

Photoactivated localization microscopy (PALM) and stochastic optical reconstruction microscopy (STORM) are two forms of super‐resolution microscopy that can resolve molecules in living cells far below the diffraction limit. Both methods rely on the spontaneous or light‐induced blinking of fluorophores such as fluorescent proteins or quantum dots. Daniels and colleagues (this issue, 1156–1164) dig deeper into cell and protein structures to further improve PALM image analysis. They developed and modeled a mathematical approach to improve discrimination of monomeric from dimeric, trimeric, and even up to multimeric photoactivatable fluorescent proteins in cells with a high expression density, which typically hampers molecule resolution. In previous work published in this journal, two of the authors (3) introduced existing internal rulers for determining fluorescent protein photoactivation efficiency that aids in the correct determination of absolute molecule numbers observed in cells by PALM. These real‐world PALM analysis examples further contribute to improving the reliability of super‐resolution microscopy and reducing misinterpretation of primary PALM data. This work is this month's Editor's Choice.

I want to draw your attention to a paper on the emerging technology of spectral flow cytometry that can be advantageous in the fight against viral disease. The October issue was dedicated to the celebration of the 40th anniversary of Optimized Multicolor Immunophenotyping Panels (OMIPs) (4) for flow cytometry. It included the first OMIP on full‐spectrum flow cytometry (5), a technology which, some predict, could become the next step in highly polychromatic (or polyparametric) cytometry (6). In the current issue, Niewold and colleagues (this issue, 1165–1179) applied spectral flow cytometry to dissect immune responses to viral diseases. In the present pandemic an important and very useful approach of imminent practical relevance is described by Cossarizza and colleagues in the (to the best of my knowledge) first publication on the immunophenotype of COVID‐19 patients (7). Technically interesting in their report is also the comparison of full‐spectrum flow cytometry with conventional four‐laser flow cytometry.

Finally, I encourage you to read in this issue more about detecting and isolating a novel type of nondoublet lymphocyte with combined T‐B properties by Burel and colleagues (this issue, 1127–1135), (8) and a parser to harmonize differences between standard and nonconformant FCS data files by Bras and colleagues (this issue, 1180–1186).

在今年 11 月期的细胞计数 A 部分中，您将找到一系列出版物，这些出版物描述了定量图像细胞计数在肿瘤细胞群研究、肿瘤细胞生理学研究和分子定位定量方面的各种应用。

肿瘤内的巨噬细胞是肿瘤进展和防御中经常被忽视的参与者。在 PubMed 中，搜索关键术语“肿瘤浸润淋巴细胞”会产生超过 11,000 个命中，而搜索“肿瘤浸润”或“肿瘤相关巨噬细胞”则仅检索到大约 5,000。Arnaud-Sampaio 及其同事将他们的综述（本期，1109-1126）致力于肿瘤相关巨噬细胞及其对肿瘤破坏或促进的影响，这取决于嘌呤能信号通路。他们使用体外模型来举例说明自动化图像细胞术在了解组织内巨噬细胞的生物学作用方面的重要性。为此，他们采用商业图像细胞计数系统来确定细胞类型和量化细胞微环境关系，并产生定量的、独立于观察者的数据（1）。

各种角蛋白类型在癌细胞恶性肿瘤中起着核心作用。Lam 及其同事（本期，1145–1155）将图像细胞计数与数字全息显微镜和细胞运动性测定相结合，研究角蛋白 19 的作用，角蛋白 19 在多种类型的癌细胞中高度表达。此前，作者 ( 2 ) 为数字全息显微镜引入了一种机器学习方法，用于对癌症和非癌细胞系进行稳健且无标记的识别和区分。在他们目前的工作中，他们专注于角蛋白 19 在癌细胞形状和运动中的作用。他们发现，通过 CRISPR 技术在乳腺癌细胞系中敲除角蛋白 19 会导致细胞形状发生变化，并且细胞运动性明显降低。

光激活定位显微镜 (PALM) 和随机光学重建显微镜 (STORM) 是两种形式的超分辨率显微镜，可以解析远低于衍射极限的活细胞中的分子。这两种方法都依赖于荧光团（如荧光蛋白或量子点）的自发或光诱导闪烁。Daniels 及其同事（本期，1156–1164）深入研究细胞和蛋白质结构，以进一步改进 PALM 图像分析。他们开发并建模了一种数学方法，以提高细胞中单体与二聚体、三聚体，甚至多聚体光活化荧光蛋白的区分，高表达密度通常会阻碍分子分辨率。在之前发表在该期刊上的工作中，两位作者 ( 3) 介绍了用于确定荧光蛋白光活化效率的现有内部标尺，有助于正确确定 PALM 在细胞中观察到的绝对分子数。这些真实的 PALM 分析示例进一步有助于提高超分辨率显微镜的可靠性并减少对主要 PALM 数据的误解。这项工作是本月的编辑选择。

我想提请您注意一篇关于光谱流式细胞术新兴技术的论文，该技术在抗击病毒性疾病方面具有优势。10 月刊旨在庆祝用于流式细胞术的优化多色免疫表型分析板 (OMIP) ( 4 ) 40 周年。它包括第一个关于全谱流式细胞术的 OMIP ( 5 )，有人预测，这项技术可能成为高度多色（或多参数）细胞术的下一步（6）。在本期中，Niewold 及其同事（本期，1165-1179）应用光谱流式细胞术来剖析对病毒性疾病的免疫反应。在当前的大流行中，Cossarizza 及其同事在（据我所知）关于 COVID-19 患者免疫表型的首次出版物中描述了一种重要且非常有用的方法，该方法具有迫在眉睫的实际意义（7）。在他们的报告中，技术上有趣的还有全光谱流式细胞术与传统四激光流式细胞术的比较。

最后，我鼓励您在本期中阅读更多有关检测和分离具有组合 T-B 特性的新型非双重淋巴细胞的内容，Burel 及其同事（本期，1127-1135），(8) 和一个解析器以协调两者之间的差异Bras 及其同事的标准和不合格 FCS 数据文件（本期，1180-1186）。