In many fields of biodiversity research, nuclear DNA content is a crucial parameter of the study organism (individual, cellular type), allowing, for example, ploidy determination, cell-cycle analysis or selecting suitable organisms and optimal strategy for whole genome sequencing (WGS). Due to lower sequencing costs, small genome size represents a major advantage for WGS projects. Not surprisingly, most DNA content estimates available for small genomes have been derived from WGS data. On the other hand, the routine use of WGS as a method for genome size estimation has been discouraged due to its poor quantification of genomic content of repetitive elements (e.g., present in centromeres or telomeres) that may significantly underestimate the true DNA amount. Currently, the most suitable method for the task is flow cytometry (FCM), a rapid and easy to perform technique, using which the DNA content is estimated from the mean fluorescence intensity of nucleic acid binding dye (e.g., propidium iodide, ethidium bromide). The FCM is routinely used in immunology, cancer research or plant and animal studies, however, its application on organisms with small genomes can be highly challenging.

Even though, the complexity of organisms is not directly linked with the amount of their nuclear DNA, the small genomes are very often found among microorganisms, specifically in nano/picoplankton, unicellular parasites and most fungi, as a consequence of the positive genome size—cell size correlation [1]. However, even microorganisms in assumed clonal populations commonly differ in morphology, physiology or biochemistry. In fungi, the smallest measured nuclear DNA content (2.2 Mbp in Encephalitozoon romaleae; [2]) also reaches the lowest end of known DNA content among all eukaryotes. Moreover, the DNA content of other fungal species is generally not much higher (with a median value <40 Mbp; [3]). In the study by Talhinhas et al. [4], the authors nicely summarized the currently used methods for fungal genome size estimation using FCM and addressed the potential pitfalls. Interestingly, these pitfalls are widely shared with many other groups of microorganisms with small genomes.

Until the modern sequencing techniques have been introduced, the microorganisms were largely understudied and their diversity, phylogenetic relationships, life cycles, and so forth widely unexplored. Despite their major importance for the global ecosystem and common applications in biotechnology, the microorganisms' research has lagged behind plant and animal studies up to the present. However, limited research of microorganisms had consequences in low number of DNA content data, especially pronounced in contrast to their estimated diversity.

Because of the small size of their bodies, microorganisms usually need to be cultivated to obtain sufficient amounts of biomass for the FCM, which is not only time-consuming but also sometimes unrealistic. For the uncultivated microorganisms in trophic interactions, another approach could be taken in simultaneous analysis of studied microorganism and its symbiont/host/prey and then to analyze these partners separately to correctly distinguish peaks of each organisms. Such approach seems especially suitable for parasites as is nicely illustrated by Talhinhas and colleagues [4] for pathogenic fungi and its host plant. However, simultaneous analysis might not be suitable for organisms substantially differing in their genome size. Moreover, microorganisms commonly live in microbial communities and this makes them harder to isolate or preserve in cultivation. Nonetheless, when possible, it is best to conduct the analysis on unistrain culture, ideally young and actively growing, as was also pointed out by Talhinhas and colleagues [4]. Unfortunately, residual of culture media may increase background fluorescence. In fact, the background noise is one of the major challenges when analyzing small genomes. For FCM analysis of plant or animals, even low sensitive flow cytometers such as CyFlow (Sysmex/Partec) are adequate, however, for FCM of microorganisms, instruments like CytoFLEX (Beckman Coulter) or FACS/LSR II (BD Biosciences), high-sensitive to small particles are more appropriate (see Figure 1). Further, there are several ways how to reduce the background noise. Nuclei should be isolated from cells, either chemically (using enzymes) or mechanically (razor-blade chopping, bead-beating). Although razor-blade chopping is routinely used in plant FCM, it seems unsuitable for protists (i.e., single-celled eukaryotes) but useful for filamentous microorganisms as was shown by Talhinhas and colleagues [4] or Čertnerová [5]. To reduce autofluorescence or adverse effect of secondary metabolites, sample can be fixed with various fixatives (ethanol, methanol, methanol: acetic acid mixture, formaldehyde, paraformaldehyde, or acetone), although the chemical fixation may not be suitable for precise genome size estimation [6]. Another possibility is to test different isolation buffers. For example, the Woody Plant Buffer or Tris-MgCl₂ buffer seems to work with fungal samples and LB01 buffer found wide application in FCM of microalgae (Talhinhas et al. [4]; Čertnerová [5]. However, new lysis buffers reflecting the specifics of particular groups of microorganisms still need to be developed. The lysis buffer may be further supplemented with PVP (polyvinylpyrrolidone) and/or with mercaptoethanol [7]. In addition, Talhinhas and colleagues [4] suggested using a lower concentration of propidium iodide, however, still adequate enough to properly stain the sample nuclei. It is also convenient to visualize measurements on a side-scatter versus fluorescence plot and apply gating to distinguish population of nuclei from a background noise if needed, as was also highlighted by the authors. In case of problematic plant or animal sample, alternative tissue/organ might help, though, this is not a possibility for most microorganisms (except few rare cases). However, despite a great effort, analyzing organisms with small genomes usually leads to higher CVs and, therefore, the criteria on acceptable precision of FCM analysis should not be generally as stringent.

Talhinhas and colleagues [4] further discussed the lack of appropriate FCM standards, which is yet another important issue accompanying analysis of small genomes. In recent years, the number of newly introduced FCM standards is slowly rising up, with, for example, Saccharomyces cerevisiae, Aspergillus fumigatus or Chlamydomonas reinhardtii possessing very small genome sizes (1C values of 24.1, 29.2, and 0.12 pg, respectively; [8, 9]). In the previous work, Talhinhas et al. [10] introduced additional fungal FCM standards with various genome sizes. Even so, there is still a dearth of FCM standards suitable for microorganisms, with those already introduced not easily accessible, leading to a frequent use of suboptimal standards such as chicken red blood cells or plant standards. However, these are biologically different and could be therefore influenced differently from analyzed sample resulting in change of sample and standard peaks proportion.

When evaluating FCM outputs, we might have to deal with some additional challenges. The DNA content data are available for only a fraction of microorganisms and thus the range of genome size variation is widely unknown but often more diverse than expected. Fungi particularly are known for their high degree of genome size plasticity. Additionally, dearth of knowledge on life cycles of the studied organisms may lead to misinterpretation of detected fluorescence peaks in FCM histograms. Some fungal species are even heterokaryotic, that is, possessing multiple different-sized nuclei, and hence generating several G₁ peaks [11]. Further, variations in chromosome number and chromosome size seem to be the rule rather than the exception [12]. Unfortunately, chromosome counts are generally problematic in microorganisms due to the small size of their cells and asynchronous cell division. This also had an impact on missing ploidy level data.

In contrast to other groups of microorganisms, fungal genome size data are listed in their own database [3]. Talhinhas and colleagues [4] analyzed these data from many different angles. They highlighted that the majority of genomes size data were obtained using WGS or static microscope-based cytometry methods, and only less than 5% were obtained with FCM. More frequent employment of FCM might thus allow researching high resolution estimates. The authors further pointed out several interesting correlations. Among others that fungal evolution toward plant mutualism or parasitism seems to be accompanied by genome size expansion and fungi interacting with plants thus possess bigger genomes when compare to saprotrophs or those interacting with animals. Similarly interesting associations with genome size were found also in different groups of microorganisms, for example, correlation of genome size with growth rate and nutritional modes in chrysophytes [13, 14]. However, much more is still waiting to be discovered with more DNA content data available for microorganisms. This could be achieved with more routine use of FCM in microorganism research so I fully support the authors' call for more frequent applications of FCM in fungal research (as well as in other microorganism studies). I also believe many of these tips might find their use in other FCM applications on microorganisms, such as detecting autofluorescence or testing cell viability.

在生物多样性研究的许多领域，核 DNA 含量是研究生物体（个体、细胞类型）的关键参数，例如，允许进行倍性测定、细胞周期分析或选择合适的生物体和全基因组测序 (WGS) 的最佳策略）。由于测序成本较低，小基因组大小代表了 WGS 项目的主要优势。毫不奇怪，大多数可用于小基因组的 DNA 含量估计都来自 WGS 数据。另一方面，由于 WGS 对重复元件（例如，存在于着丝粒或端粒中）的基因组含量的定量较差，可能会显着低估真实的 DNA 量，因此不鼓励常规使用 WGS 作为基因组大小估计的方法。目前，最适合该任务的方法是流式细胞术（FCM），一种快速且易于执行的技术，使用该技术从核酸结合染料（例如，碘化丙锭、溴化乙锭）的平均荧光强度估计 DNA 含量。FCM 通常用于免疫学、癌症研究或植物和动物研究，然而，它在具有小基因组的生物体上的应用可能极具挑战性。

尽管生物体的复杂性与其核 DNA 的数量没有直接关系，但由于基因组大小为正，小基因组经常在微生物中发现，特别是在纳米/微型浮游生物、单细胞寄生虫和大多数真菌中——细胞大小相关性 [ 1 ]。然而，即使是假定克隆种群中的微生物，通常在形态、生理学或生物化学方面也存在差异。在真菌中，测得的最小核 DNA 含量（Encephalitozoon romaleae中为 2.2 Mbp ；[ 2 ]）也达到了所有真核生物中已知 DNA 含量的最低值。此外，其他真菌物种的 DNA 含量一般不会高很多（中值 <40 Mbp；[ 3 ]）。在 Talhinhas 等人的研究中。[4 ]，作者很好地总结了目前使用 FCM 估计真菌基因组大小的方法，并解决了潜在的缺陷。有趣的是，这些陷阱与许多其他具有小基因组的微生物群广泛存在。

在引入现代测序技术之前，微生物在很大程度上还没有得到充分研究，它们的多样性、系统发育关系、生命周期等还没有被广泛探索。尽管它们对全球生态系统和生物技术的常见应用具有重要意义，但迄今为止，微生物的研究一直落后于植物和动物研究。然而，对微生物的有限研究导致 DNA 含量数据数量少，与它们估计的多样性相比尤其明显。

由于它们的身体体积小，通常需要培养微生物以获得足够量的生物量用于FCM，这不仅耗时而且有时不切实际。对于营养相互作用中的未培养微生物，可以采取另一种方法同时分析所研究的微生物及其共生体/宿主/猎物，然后分别分析这些伙伴，以正确区分每种生物的峰值。这种方法似乎特别适用于寄生虫，正如 Talhinhas 及其同事 [ 4] 用于病原真菌及其寄主植物。然而，同时分析可能​​不适用于基因组大小差异很大的生物体。此外，微生物通常生活在微生物群落中，这使得它们在培养过程中更难分离或保存。尽管如此，在可能的情况下，最好对单一文化进行分析，最好是年轻且积极成长的文化，正如 Talhinhas 及其同事所指出的那样 [ 4]。不幸的是，培养基的残留可能会增加背景荧光。事实上，背景噪声是分析小型基因组时的主要挑战之一。对于植物或动物的 FCM 分析，即使是低灵敏度的流式细胞仪，如 CyFlow (Sysmex/Partec) 也足够了，但是，对于微生物的 FCM，仪器如 CytoFLEX (Beckman Coulter) 或 FACS/LSR II (BD Biosciences)，高灵敏度对小颗粒敏感更合适（见图1）。此外，有几种方法可以减少背景噪声。细胞核应该从细胞中分离出来，无论是化学的（使用酶）还是机械的（剃须刀切碎，珠子敲打）。虽然剃刀刀片切割通常用于植物 FCM，但它似乎不适合原生生物（即4 ] 或 Čertnerová [ 5 ]。为了减少自发荧光或次级代谢物的不利影响，可以用各种固定剂（乙醇、甲醇、甲醇：乙酸混合物、甲醛、多聚甲醛或丙酮）固定样品，尽管化学固定可能不适合精确的基因组大小估计 [ 6 ]。另一种可能性是测试不同的隔离缓冲液。例如，木本植物缓冲液或 Tris-MgCl ₂缓冲液似乎适用于真菌样品，而 LB01 缓冲液在微藻的 FCM 中得到广泛应用（Talhinhas 等人 [ 4 ]；Čertnerová [ 5 ]]。然而，仍需要开发反映特定微生物群特征的新裂解缓冲液。裂解缓冲液可以进一步补充 PVP（聚乙烯吡咯烷酮）和/或巯基乙醇 [ 7 ]。此外，Talhinhas 及其同事 [ 4] 建议使用较低浓度的碘化丙啶，但仍足以正确染色样品核。还可以方便地在侧向散射与荧光图上可视化测量，并在需要时应用门控来区分核群和背景噪声，作者也强调了这一点。如果植物或动物样本有问题，替代组织/器官可能会有所帮助，但是，这对大多数微生物来说是不可能的（少数罕见情况除外）。然而，尽管付出了巨大的努力，但分析具有小基因组的生物体通常会导致更高的 CV，因此，FCM 分析可接受精度的标准通常不应该那么严格。

Talhinhas 及其同事 [ 4 ] 进一步讨论了缺乏适当的 FCM 标准，这是伴随小基因组分析的另一个重要问题。近年来，新引入的 FCM 标准的数量正在缓慢上升，例如，酿酒酵母、烟曲霉或莱茵衣藻具有非常小的基因组大小（1C 值分别为 24.1、29.2 和 0.12 pg；[ 8 , 9 ])。在之前的工作中，Talhinhas 等人。[ 10] 引入了具有不同基因组大小的其他真菌 FCM 标准。即便如此，仍然缺乏适用于微生物的 FCM 标准，那些已经引入的标准不容易获得，导致经常使用次优标准，例如鸡红细胞或植物标准。然而，这些在生物学上是不同的，因此可能会受到与分析样品不同的影响，从而导致样品和标准峰比例的变化。

在评估 FCM 输出时，我们可能需要处理一些额外的挑战。DNA 含量数据仅可用于一小部分微生物，因此基因组大小变异的范围广为人知，但通常比预期的更加多样化。真菌尤其以其高度的基因组大小可塑性而闻名。此外，缺乏对所研究生物体生命周期的了解可能会导致对 FCM 直方图中检测到的荧光峰的误解。一些真菌物种甚至是异核的，即拥有多个不同大小的细胞核，因此会产生多个 G ₁峰 [ 11 ]。此外，染色体数量和染色体大小的变化似乎是规则而不是例外 [ 12]。不幸的是，由于微生物的细胞体积小和细胞分裂不同步，染色体计数通常在微生物中存在问题。这也对缺失的倍性水平数据产生了影响。

与其他微生物群相比，真菌基因组大小数据列在它们自己的数据库中 [ 3 ]。Talhinhas 及其同事 [ 4] 从许多不同的角度分析了这些数据。他们强调，大多数基因组大小数据是使用 WGS 或基于静态显微镜的流式细胞仪方法获得的，而使用 FCM 获得的只有不到 5%。因此，更频繁地使用 FCM 可能允许研究高分辨率估计。作者进一步指出了几个有趣的相关性。其中，真菌向植物共生或寄生的进化似乎伴随着基因组大小的扩大，因此与腐生菌或与动物相互作用的真菌相比，与植物相互作用的真菌具有更大的基因组。在不同的微生物群中也发现了与基因组大小相似的有趣关联，例如，金黄植物的基因组大小与生长速率和营养模式的相关性 [ 13, 14]。然而，随着更多可用于微生物的 DNA 含量数据，还有更多的东西有待发现。这可以通过在微生物研究中更常规地使用 FCM 来实现，因此我完全支持作者呼吁在真菌研究（以及其他微生物研究）中更频繁地应用 FCM。我也相信这些技巧中的许多可能会在微生物的其他 FCM 应用中找到它们的用途，例如检测自发荧光或测试细胞活力。