1.1 如果理解其局限性，本实践描述了使用图像分析定量测量来自干细胞或祖细胞群体的菌落数量和生物学特性的程序。 1.2 该实践应用于 体外 实验室设置。 1.3 这种做法利用了:( 一 )细胞和集落图像采集的标准化协议 体外 在定义的视野（FOV）中处理定义的起始细胞群，以及( b )图像处理和分析的标准协议。 1.4 相关FOV可能是二维或三维的，具体取决于所询问的CFU分析系统。 1.5 分析结果中使用的主要单位是视野中存在的菌落数。 此外，还可以基于现存的形态特征、分布特性或使用二级标记（例如染色或标记方法）诱导的特性来评估视野内单个菌落和细胞的特征和子分类。 1.6 成像方法要求以足够的分辨率捕获相关FOV的图像，以便能够检测和表征单个细胞，并在FOV上足够检测、区分和表征菌落作为完整对象进行评估。 1.7 适用于二维和三维数据集的图像处理程序用于将细胞或菌落识别为视野内的离散对象。成像方法可以针对多种细胞类型和细胞特征进行优化，使用用于分割和聚类的分析工具，以指示共享谱系关系（即单个原始干细胞或祖细胞的克隆扩增）的方式，通过邻近性或形态学定义彼此相关的细胞组。 1.8 单个群体对象的特征（每个群体的细胞数、细胞密度、细胞大小、细胞分布、细胞异质性、细胞基因型或表型，以及次级标记物的表达模式、分布和强度）是克隆后代潜在生物学特性差异的信息。 1.9 在适当控制的实验条件下，菌落之间的差异可以为初始群体中的菌落形成细胞（CFU）的生物学特性和潜在异质性提供信息。 1.10 细胞和集落面积/体积、数量等可表示为细胞培养面积（平方毫米）或初始细胞悬浮液体积（毫升）的函数。 1.11 使用两种或两种以上光学方法对FOV进行顺序成像可能有助于积累有关样本中单个细胞或菌落物体的定量信息。 此外，在过程跟踪和验证设置中，需要对同一样本进行重复成像。因此，本实践要求使用定义的坐标系，对视野区域/体积内的细胞和菌落（质心）位置进行可重复识别。 1.12 为了获得足够大的视野（FOV），可以在高放大率下将足够分辨率的图像捕捉为多个图像场/图像块，然后将其组合在一起，形成代表整个细胞培养区域的马赛克。 1.13 对组织工程、再生医学和细胞治疗中常用的细胞和组织进行常规分析，以确定原始干细胞和祖细胞群的数量、患病率、生物学特征和生物学潜力。 1.13.1 常见的适用细胞类型和细胞来源包括但不限于:哺乳动物干细胞和祖细胞；成人来源细胞（例如，血液、骨髓、皮肤、脂肪、肌肉、粘膜）细胞、胎儿来源细胞（例如，脐血、胎盘/脐带、羊水）；胚胎干细胞（ESC）（即来自囊胚的内部细胞团）；诱导多能干细胞（iPC）（例如，重编程的成年细胞）；培养扩增细胞；以及特定类型组织的终末分化细胞。 1.13.2 与克隆集落内和克隆集落间分化检测相关的成熟分化表型的常见应用示例包括:造血表型（红细胞、淋巴细胞、中性粒细胞、嗜酸性粒细胞、嗜碱性粒细胞、单核细胞、巨噬细胞等）、成体组织- 特定祖细胞表型（成卵细胞、软骨细胞、脂肪细胞等）和其他组织（肝细胞、神经元、内皮细胞、角质形成细胞、胰岛等）。 1.14 可以测定各种组织中干细胞和祖细胞的数量 体外 通过使用保持潜在干细胞和/或祖细胞群的活力和生物潜力的方法将细胞从组织中解放出来，并将组织衍生细胞置于 体外 导致干细胞和祖细胞作为克隆集落有效激活和增殖的环境。因此，可以根据观察到的已形成的集落形成单元（观察到的集落形成单元（oCFU））的数量来估计干细胞和祖细胞的真实数量（真实集落形成单元（tCFU）） ( 1- 3. ) 2. ( 图A1.1 ). 干细胞和/或祖细胞的患病率可以根据检测到的观察到的集落形成单位（oCFU）的数量除以检测的总细胞数来估计。 1.15 细胞和菌落计数的自动图像采集和分析方法（本文中所述）已得到验证，并发现与当前的“金标准”相比，该“金标准”提供了更高的准确性和精确度，即在带或不带血细胞计的亮场或荧光显微镜下手动观察者定义的视觉细胞和菌落计数 ( 4. ) ，减少观察者内和观察者间的变化。过去，有几个小组曾试图将这一过程和/或类似过程自动化 ( 5. , 6. ) . 最近的报告进一步证明了在细胞甚至核水平上提取各种细胞类型菌落的定性和定量数据的能力 ( 4. , 7. ) . 1.16 软件和硬件的进步现在广泛地支持系统自动化分析方法。这种不断发展的技术需要就本实践中提出的测量单位、术语、过程定义和分析解释达成一致。 1.17 自动化CFU分析的标准化方法为提高CFU分析在几个科学和商业领域的价值和效用提供了机会: 1.17.1 自动化CFU分析的标准化方法为提高CFU分析方法的特异性提供了机会，通过优化可推广的方案和特定细胞类型的定量指标，以及可以在不同实验室之间统一应用的CFU分析系统。 1.17.2 自动化CFU分析的标准化方法为通过增加吞吐量和减少工作流需求来降低生物科学各个方面的菌落分析成本提供了机会。 1.17.3 自动CFU分析的标准化方法为提高寻求检测CFU影响的实验系统的灵敏度和特异性提供了机会 体外 条件、生物刺激、生物材料和 体外 干细胞和祖细胞附着、迁移、增殖、分化和存活的处理步骤。 1.18 限制描述如下: 1.18.1 菌落识别细胞来源/菌落类型/标记变异性- 来自不同组织来源和不同来源的干细胞和祖细胞 体外 环境将表现出不同的生物学特征。因此，检测细胞或细胞核的具体方法和使用的二级标记及其各自染色方案的实施将因CFU分析系统、细胞类型和被询问的标记而异。 用于检测细胞和菌落、定义菌落对象和表征菌落对象的图像捕获和图像分析的优化协议将因使用的细胞源和CFU系统而异。这些协议将需要在每个应用中独立优化、表征和验证。然而，一旦定义，这些可以在实验室之间以及跨临床和研究领域进行推广。 1.18.2 仪器引起的图像捕获变化- 如果没有正确处理，上述图像采集组件的选择可能会对细胞分割和随后的菌落识别产生不利影响。例如，使用水银灯泡而不是光纤荧光光源或光学系统的普遍错位可能会产生不均匀照明或包含主要大视场图像的平铺图像的渐晕。 这可以通过在拼接瓷砖之前对大视场图像中的每个瓷砖应用背景减法例程来纠正。 1.18.3 CFU检测系统相关成像伪影的变化- 除了呈现具有定义菌落识别所必须使用的独特特征的菌落对象外，每个CFU系统的每个图像可能呈现非细胞和非菌落伪影（例如，细胞碎片、绒毛、玻璃像差、反射、自体荧光等），如果未识别和管理，可能会混淆细胞和菌落的检测。 1.18.4 图像捕获方法和质量控制变化- 图像质量的变化将显著影响图像分析方法的精度和再现性。焦点、照明、瓷砖配准、曝光时间、淬火和发射光谱泄漏的变化都是对图像质量和再现性的重要潜在限制或威胁。 1.19 以国际单位制表示的数值应视为标准值。本标准不包括其他计量单位。 1.20 本标准并非旨在解决与其使用相关的所有安全问题（如有）。本标准的用户有责任在使用前制定适当的安全和健康实践，并确定监管限制的适用性。 1.21 本国际标准是根据世界贸易组织技术性贸易壁垒（TBT）委员会发布的《关于制定国际标准、指南和建议的原则的决定》中确立的国际公认标准化原则制定的。 ====意义和用途====== 4.1 手动观察者- 依赖性测定- 基于观察者相关标准或判断的细胞和CFU培养的手动量化是一项极其繁琐和耗时的任务，并且受到用户偏好的显著影响。为了保持数据采集的一致性，利用基于细胞和菌落的分析进行的药理学和药物发现与开发研究通常需要一个观察者对数百种甚至数千种培养物中的细胞和菌落进行计数。由于观察者疲劳，量化的准确性和再现性都受到严重影响 ( 5. ) . 当使用多个观察者时，观察者疲劳会减少，但由于观察者偏差和显著的内部差异，细胞和菌落计数的准确性和再现性仍然显著受损- 和观察者之间的可变性 ( 2. , 4. ) . 定量自动图像分析的使用提供了菌落数量以及每个菌落中细胞数量的数据。这些数据也可用于计算每个菌落的平均细胞数。通过手工计数和细胞数量测定（例如DNA或线粒体）对菌落进行量化的传统方法仍然是一种可行的方法，可用于计算每个菌落的平均细胞数。这些传统方法的优点是目前劳动密集度较低，技术要求较低 ( 8. , 9 ) . 然而，传统的分析方法既不能提供菌落水平的信息（例如，变异和偏斜），也不能提供将不属于菌落一部分的细胞排除在平均菌落大小计算之外的方法。 因此，当样本中的大量细胞与菌落形成无关时，从这些替代方法获得的每个菌落的平均细胞数的测量可能不同。通过使用最先进的图像采集、处理和分析硬件和软件，实现了准确、精确、鲁棒和自动化的分析系统。 4.1.1 应用领域- 细胞和菌落计数（CFU分析）在基于细胞的再生医学和细胞治疗的生产、质量保证/控制（QA/QC）以及产品安全性和效力释放标准的开发中变得尤为重要。美国食品和药物管理局（FDA）有一份指导文件，表明CFU测定可能适用于检测胎盘和脐带血的稳定性- 衍生干细胞 ( 7. ) . 由于细胞源验证和质量保证/质量控制约由50 % 细胞疗法的制造成本( 10 )因此，开发一种精确、可靠且经济高效的细胞和菌落计数方法对该行业的可持续性和增长至关重要。菌落形成单位分析自动化分析的广泛应用领域包括: 4.1.1.1 通过将生物潜能和功能潜能与CFU形成相关来表征细胞源。 4.1.1.2 描述处理步骤或生物或物理操作（例如刺激）对细胞或菌落形成的影响。 4.1.1.3 使用特定荧光和非荧光（分化）标记的细胞和菌落特征。 4.1.1.4 从应用集落形成试验推断较大样本的生物效价（例如，分化、增殖等）- 样品。 4.1.1.5 提供首选菌落（特定组织类型、增殖率等）的亚菌落选择标准，以供使用和/或进一步扩展。 4.2 技术（图像采集、处理和分析）- 当前的标准利用用户输入来定义菌落的存在和位置，其基于通过显微镜目镜在低倍下对整个培养表面的可视化。在这种情况下，可以在透射光模式下（未染色或使用组织化学标记）或使用染料或抗体进行荧光观察。在这种情况下，菌落数是唯一可测量的输出参数。利用基于显微镜的成像系统将高分辨率图像拼接成整个培养表面的单个马赛克图像，然后使用图像处理算法“聚类”分割的细胞以描绘菌落，提供了一种全自动、准确的、，以及表征培养细胞的生物潜力和功能潜能的精确方法。 此外，除了菌落数之外，提取的参数还提供了进一步表征和分类菌落级统计的手段。这些参数包括但不限于细胞/核计数、细胞/核密度、菌落形态（形状和大小参数）、二级标记覆盖率、有效增殖率等( 图A1.2 ). 除了来源于骨、骨髓、软骨、脂肪组织、肌肉、骨膜和滑膜的人类结缔组织祖细胞（CTPs）外，该实践和技术已应用于多种细胞和组织类型的细胞和菌落鉴定和表征，包括:脐血造血干细胞( 图X1.2 ); 脂肪干细胞( 图X1.3 ); 和人类表皮( 图X1.4 )和皮肤( 图X1.5 )干细胞。 4.3 CFU分析自动化分析的好处- 与使用显微镜和血细胞仪手动计数细胞和菌落相比，自动化分析有望提供更快速、可重复和精确的结果。除了耗时、劳动密集和主观之外，人工计数还具有显著的观察者内和观察者间变异性，变异系数（CV）从8.1%到40.0不等 % 22.7%-80% %, 分别地细胞活力评估和祖细胞（菌落）类型计数的标准CV范围为19.4%-42.9% % 46.6%-100% %, 分别地 ( 4. , 11 , 12 ) . 相比之下，针对细菌、骨髓的研究- 衍生干细胞和成骨祖细胞共同得出结论，与传统的手动方法相比，自动计数为细胞和菌落的计数和大小确定提供了更高的准确性、精确度和/或速度 ( 4- 6. ) . 细胞和菌落计数的自动化方法具有更少的偏见、更少的时间消耗和更少的劳动，并为细胞和菌落类型和形态的内在特征提供更多的定性和定量数据。 4.4 细胞培养表面积和最佳细胞接种密度的选择- 当执行CFU分析时，优化 细胞培养表面积 和 细胞接种密度 对于开发生成可靠且可重复的菌落和细胞水平数据的方法至关重要。如果播种密度过低，则观察到的菌落频率会降低。 这可能导致样本量不足以表征样本中CFU的数量。如果播种密度太高，形成的菌落可能间隔太近。重叠的菌落足迹损害了菌落计数和表征。由于给定细胞源中CFU患病率的固有范围可能差异很大，在许多情况下，有必要采用试错法来优化给定细胞源所需的细胞接种密度（或密度范围）。值得注意的是，细胞来源（例如骨髓）的异质性越大，准确表示干细胞和祖细胞成分所需的菌落就越多。此外，细胞类型、有效增殖率（EPR）和特定细胞培养条件（例如，培养基、血清、因子、氧张力等）可以影响菌落形成。 例如，中描述的自动CFU分析 图A1.2 雇佣六天的文化期，两次媒体更换，20 % 氧气张力，alpha MEM介质（含25 % 胎牛血清、抗坏血酸、地塞米松和链霉素），优化的细胞接种密度为250 每厘米000个有核细胞 2. (250 每1ml细胞培养基中有1000个细胞），细胞培养表面积为22 mm×22 mm（双室Lab Tek培养玻片） ( 12 , 13 ) . 4.5 有用的文档- 有许多有用的文档介绍了使用成像方法进行细胞定量测量的最佳实践:指南 F2998 指导 F3294 ，ISO 20391-1，ISO 20391-2和“FDA数字病理学全玻片成像设备技术性能评估指南，” ( 14 ) .

1.1 This practice, provided its limitations are understood, describes a procedure for quantitative measurement of the number and biological characteristics of colonies derived from a stem cell or progenitor population using image analysis. 1.2 This practice is applied in an in vitro laboratory setting. 1.3 This practice utilizes: ( a ) standardized protocols for image capture of cells and colonies derived from in vitro processing of a defined population of starting cells in a defined field of view (FOV), and ( b ) standardized protocols for image processing and analysis. 1.4 The relevant FOV may be two-dimensional or three-dimensional, depending on the CFU assay system being interrogated. 1.5 The primary unit to be used in the outcome of analysis is the number of colonies present in the FOV. In addition, the characteristics and sub-classification of individual colonies and cells within the FOV may also be evaluated, based on extant morphological features, distributional properties, or properties elicited using secondary markers (for example, staining or labeling methods). 1.6 Imaging methods require that images of the relevant FOV be captured at sufficient resolution to enable detection and characterization of individual cells and over a FOV that is sufficient to detect, discriminate between, and characterize colonies as complete objects for assessment. 1.7 Image processing procedures applicable to two- and three-dimensional data sets are used to identify cells or colonies as discreet objects within the FOV. Imaging methods may be optimized for multiple cell types and cell features using analytical tools for segmentation and clustering to define groups of cells related to each other by proximity or morphology in a manner that is indicative of a shared lineage relationship (that is, clonal expansion of a single founding stem cell or progenitor). 1.8 The characteristics of individual colony objects (cells per colony, cell density, cell size, cell distribution, cell heterogeneity, cell genotype or phenotype, and the pattern, distribution and intensity of expression of secondary markers) are informative of differences in underlying biological properties of the clonal progeny. 1.9 Under appropriately controlled experimental conditions, differences between colonies can be informative of the biological properties and underlying heterogeneity of colony founding cells (CFUs) within a starting population. 1.10 Cell and colony area/volume, number, and so forth may be expressed as a function of cell culture area (square millimeters), or initial cell suspension volume (milliliters). 1.11 Sequential imaging of the FOV using two or more optical methods may be valuable in accumulating quantitative information regarding individual cells or colony objects in the sample. In addition, repeated imaging of the same sample will be necessary in the setting of process tracking and validation. Therefore, this practice requires a means of reproducible identification of the location of cells and colonies (centroids) within the FOV area/volume using a defined coordinate system. 1.12 To achieve a sufficiently large field-of-view (FOV), images of sufficient resolution may be captured as multiple image fields/tiles at high magnification and then combined together to form a mosaic representing the entire cell culture area. 1.13 Cells and tissues commonly used in tissue engineering, regenerative medicine, and cellular therapy are routinely assayed and analyzed to define the number, prevalence, biological features, and biological potential of the original stem cell and progenitor population(s). 1.13.1 Common applicable cell types and cell sources include, but are not limited to: mammalian stem and progenitor cells; adult-derived cells (for example, blood, bone marrow, skin, fat, muscle, mucosa) cells, fetal-derived cells (for example, cord blood, placental/cord, amniotic fluid); embryonic stem cells (ESC) (that is, derived from inner cell mass of blastocysts); induced pluripotent cells (iPC) (for example, reprogrammed adult cells); culture expanded cells; and terminally differentiated cells of a specific type of tissue. 1.13.2 Common applicable examples of mature differentiated phenotypes which are relevant to detection of differentiation within and among clonal colonies include: hematopoietic phenotypes (erythrocytes, lymphocytes, neutrophiles, eosinophiles, basophiles, monocytes, macrophages, and so forth), adult tissue-specific progenitor cell phenotypes (oteoblasts, chondrocytes, adipocytes, and so forth), and other tissues (hepatocytes, neurons, endothelial cells, keratinocyte, pancreatic islets, and so forth). 1.14 The number of stem cells and progenitor cells in various tissues can be assayed in vitro by liberating the cells from the tissues using methods that preserve the viability and biological potential of the underlying stem cell and/or progenitor population, and placing the tissue-derived cells in an in vitro environment that results in efficient activation and proliferation of stem and progenitor cells as clonal colonies. The true number of stem cells and progenitors (true colony forming units (tCFU)) can thereby be estimated on the basis of the number of colony-forming units observed (observed colony forming units (oCFU)) to have formed ( 1- 3 ) 2 ( Fig. A1.1 ). The prevalence of stem cells and/or progenitors can be estimated on the basis of the number of observed colony-forming units (oCFU) detected, divided by the number of total cells assayed. 1.15 The automated image acquisition and analysis approach (described herein) to cell and colony enumeration has been validated and found to provide superior accuracy and precision when compared to the current “gold standard” of manual observer defined visual cell and colony counting under a brightfield or fluorescent microscope with or without a hemocytometer ( 4 ) , reducing both intra- and inter-observer variation. Several groups have attempted to automate this and/or similar processes in the past ( 5 , 6 ) . Recent reports further demonstrate the capability of extracting qualitative and quantitative data for colonies of various cell types at the cellular and even nuclear level ( 4 , 7 ) . 1.16 Advances in software and hardware now broadly enable systematic automated analytical approaches. This evolving technology creates the need for general agreement on units of measurement, nomenclature, process definitions, and analytical interpretation as presented in this practice. 1.17 Standardized methods for automated CFU analysis open opportunities to enhance the value and utility of CFU assays in several scientific and commercial domains: 1.17.1 Standardized methods for automated CFU analysis open opportunities to advance the specificity of CFU analysis methods though optimization of generalizable protocols and quantitative metrics for specific cell types and CFU assay systems which can be applied uniformly between disparate laboratories. 1.17.2 Standardized methods for automated CFU analysis open opportunities to reduce the cost of colony analysis in all aspects of biological sciences by increasing throughput and reducing work flow demands. 1.17.3 Standardized methods for automated CFU analysis open opportunities to improve the sensitivity and specificity of experimental systems seeking to detect the effects of in vitro conditions, biological stimuli, biomaterials and in vitro processing steps on the attachment, migration, proliferation, differentiation, and survival of stem cells and progenitors. 1.18 Limitations are described as follows: 1.18.1 Colony Identification—Cell Source/Colony Type/Marker Variability— Stem cells and progenitors from various tissue sources and in different in vitro environments will manifest different biological features. Therefore, the specific means to detect cells or nuclei and secondary markers utilized and the implementation of their respective staining protocols will differ depending on the CFU assay system, cell type(s) and markers being interrogated. Optimized protocols for image capture and image analysis to detect cells and colonies, to define colony objects and to characterize colony objects will vary depending on the cell source being utilized and CFU system being used. These protocols will require independent optimization, characterization and validation in each application. However, once defined, these can be generalized between labs and across clinical and research domains. 1.18.2 Instrumentation-Induced Variability in Image Capture— Choice of image acquisition components described above may adversely affect segmentation of cells and subsequent colony identification if not properly addressed. For example, use of a mercury bulb rather than a fiber-optic fluorescent light source or the general misalignment of optics could produce uneven illumination or vignetting of tiled images comprising the primary large FOV image. This may be corrected by applying background subtraction routines to each tile in a large FOV image prior to tile stitching. 1.18.3 CFU Assay System Associated Variation in Imaging Artifacts— In addition to the presentation of colony objects with unique features that must be utilized to define colony identification, each image from each CFU system may present non-cell and non-colony artifacts (for example, cell debris, lint, glass aberrations, reflections, autofluorescence, and so forth) that may confound the detection of cells and colonies if not identified and managed. 1.18.4 Image Capture Methods and Quality Control Variation— Variation in image quality will significantly affect the precision and reproducibility of image analysis methods. Variation in focus, illumination, tile registration, exposure time, quenching, and emission spectral bleeding, are all important potential limitations or threats to image quality and reproducibility. 1.19 The values stated in SI units are to be regarded as standard. No other units of measurement are included in this standard. 1.20 This standard does not purport to address all of the safety concerns, if any, associated with its use. It is the responsibility of the user of this standard to establish appropriate safety and health practices and determine the applicability of regulatory limitations prior to use. 1.21 This international standard was developed in accordance with internationally recognized principles on standardization established in the Decision on Principles for the Development of International Standards, Guides and Recommendations issued by the World Trade Organization Technical Barriers to Trade (TBT) Committee. ====== Significance And Use ====== 4.1 The Manual Observer-Dependent Assay— The manual quantification of cell and CFU cultures based on observer-dependent criteria or judgment is an extremely tedious and time-consuming task and is significantly impacted by user bias. In order to maintain consistency in data acquisition, pharmacological and drug discovery and development studies utilizing cell- and colony-based assays often require that a single observer count cells and colonies in hundreds, and potentially thousands of cultures. Due to observer fatigue, both accuracy and reproducibility of quantification suffer severely ( 5 ) . When multiple observers are employed, observer fatigue is reduced, but the accuracy and reproducibility of cell and colony enumeration is still significantly compromised due to observer bias and significant intra- and inter-observer variability ( 2 , 4 ) . Use of quantitative automated image analysis provides data for both the number of colonies as well as the number of cells in each colony. These data can also be used to calculate mean cells per colony. Traditional methods for quantification of colonies by hand-counting coupled with an assay for cell number (for example, DNA or mitochondrial) remains a viable method that can be used to calculate the mean number of cells per colony. These traditional methods have the advantage that they are currently less labor intensive and less technically demanding ( 8 , 9 ) . However, the traditional assays do not, provide colony level information (for example, variation and skew), nor do they provide a means for excluding cells that are not part of a colony from the calculation of mean colony size. As a result, the measurement of the mean number of cells per colony that is obtained from these alternative methods may differ when substantial numbers of cells in a sample are not associated with colony formation. By employing state-of-the-art image acquisition, processing and analysis hardware and software, an accurate, precise, robust and automated analysis system is realized. 4.1.1 Areas of Application— Cell and colony enumeration (CFU assay) is becoming particularly important in the manufacture, quality assurance/control (QA/QC), and development of product safety and potency release criteria for cell-based regenerative medicine and cellular therapy. The U.S. Food and Drug Administration (FDA) has a guidance document that indicates that the CFU assay may be appropriate for testing stability of placental and umbilical cord blood-derived stem cells ( 7 ) . Since cell source validation and QA/QC comprise approximately 50 % of the manufacturing cost of cellular therapies ( 10 ), developing a precise, robust, and cost-effective means for enumerating cells and colonies is vital to sustainability and growth in this industry. The broad areas of use for automated analysis of colony forming unit assays include: 4.1.1.1 Characterization of a cell source by correlating biological potential and functional potency with CFU formation. 4.1.1.2 Characterization of the effect of processing steps or biological or physical manipulation (for example, stimuli) on cells or colony formation. 4.1.1.3 Cell and colony characterization using specific fluorescent and non-fluorescent (differentiation) markers. 4.1.1.4 Extrapolation of the biological potency (for example, differentiation, proliferative, and so forth) of a larger sample from application of colony forming assay to sub-samples. 4.1.1.5 Provision of criteria for sub-colony selection of preferred colonies (specific tissue type, proliferation rate, and so forth) for use and/or further expansion. 4.2 The Technology (image acquisition, processing, and analysis)— Current standards utilize user input for defining the presence and location of colonies based on visualization of an entire culture surface at low magnification through the eyepieces of a microscope. In this case, the sample may be viewed in transmission light mode (unstained or with a histochemical marker) or fluorescently with a dye or antibody. For this practice, the colony count is the only measurable output parameter. Utilizing a microscope-based imaging system to stitch together high resolution image tiles into a single mosaic image of the entire culture surface and subsequently “clustering” segmented cells using image processing algorithms to delineate colonies, provides a fully automated, accurate, and precise method for characterizing the biological potential and functional potency of the cultured cells. Furthermore, extracted parameters in addition to colony number provide means of further characterization and sub-classification of colony level statistics. These parameters include, but are not limited to, cell/nuclear count, cell/nuclear density, colony morphology (shape and size parameters), secondary marker coverage, effective proliferation rates, and so forth ( Fig. A1.2 ). In addition to human connective tissue progenitors (CTPs) derived from bone, bone marrow, cartilage, adipose tissue, muscle, periosteum, and synovium, this practice and technology has been implemented in the cell and colony identification and characterization of several cell and tissue types including: umbilical cord blood hematopoietic stem cells ( Fig. X1.2 ); adipose-derived stem cells ( Fig. X1.3 ); and human epidermal ( Fig. X1.4 ) and dermal ( Fig. X1.5 ) stem cells. 4.3 Benefits of Automated Analysis of CFU Assays— Automated analysis is expected to provide more rapid, reproducible, and precise results in comparison to the manual enumeration of cells and colonies utilizing a microscope and hemocytometer. In addition to being time consuming, labor intensive, and subjective, manual enumeration has been shown to have a significant degree of intra- and inter-observer variability, with coefficients of variation (CV) ranging from 8.1 % to 40.0 % and 22.7 % to 80 %, respectively. Standard CVs for cell viability assessment and progenitor (colony) type enumeration have been shown to range from 19.4 % to 42.9 % and 46.6 % to 100 %, respectively ( 4 , 11 , 12 ) . In contrast, studies focusing on bacteria, bone marrow-derived stem cells and osteogenic progenitor cells have collectively concluded that automated enumeration provides significantly greater accuracy, precision, and/or speed for counting and sizing cells and colonies, relative to conventional manual methodologies ( 4- 6 ) . Automated methods for enumerating cells and colonies are less biased, less time consuming, less laborious, and provide greater qualitative and quantitative data for intrinsic characteristics of cell and colony type and morphology. 4.4 Selection of Cell Culture Surface Area and Optimal Cell Seeding Density— When performing a CFU assay, optimizing the cell culture surface area and cell seeding density is critical to developing methods for generating reliable and reproducible colony- and cell-level data. If seeding density is too low, then the frequency of observed colonies is decreased. This can result in a sampling size that is inadequate to characterize the population of CFUs in the sample. If seeding density is too high, the colonies that are formed may be too closely spaced. Overlapping colony footprints compromise colony counting and characterization. Because the intrinsic range of CFU prevalence in a given cell source may vary widely, in many cases, a trial and error approach to optimizing cell seeding density (or range of densities) that are needed for a given cell source will be necessary. It is important to note that the more heterogeneous the cell source (for example, bone marrow), the more colonies that are needed to accurately represent the stem and progenitor cell constituents. Further, the cell type, effective proliferation rate (EPR) and specific cell culture conditions (for example, media, serum, factors, oxygen tension, and so forth) can impact colony formation. For example, the automated CFU assay depicted in Fig. A1.2 employs a six-day culture period, two media changes, 20 % oxygen tension, alpha-MEM media (with 25 % fetal bovine serum, ascorbate, dexamethasone and streptomycin), an optimized cell seeding density of 250 000 nucleated cells per cm 2 (250 000 cells per 1 mL of cell culture medium) and a cell culture surface area of 22 mm by 22 mm (dual-chamber Lab-Tek culture slides) ( 12 , 13 ) . 4.5 Useful Documents— A number of useful documents are available that address best practices for conducting quantitative measurements of cells using imaging approaches: Guide F2998 , Guide F3294 , ISO 20391-1, ISO 20391-2, and “FDA Guidance on Technical Performance Assessment of Digital Pathology Whole Slide Imaging Devices,” ( 14 ) .