ASTM F2944-2012 Standard Test Method for Automated Colony Forming Unit (CFU) AssaysImage Acquisition and Analysis Method for Enumerating and Characterizing Cells and Colonies in Cu.pdf

1、Designation: F2944 12Standard Test Method forAutomated Colony Forming Unit (CFU) AssaysImageAcquisition and Analysis Method for Enumerating andCharacterizing Cells and Colonies in Culture1This standard is issued under the fixed designation F2944; the number immediately following the designation indi

2、cates the year oforiginal adoption or, in the case of revision, the year of last revision. A number in parentheses indicates the year of last reapproval. Asuperscript epsilon () indicates an editorial change since the last revision or reapproval.1. Scope1.1 This test method, provided its limitations

3、 are under-stood, describes a procedure for quantitative measurement ofthe number and biological characteristics of colonies derivedfrom a stem cell or progenitor population using image analysis.1.2 This test method is applied in an in vitro laboratorysetting.1.3 This method utilizes: (a) standardiz

4、ed protocols forimage capture of cells and colonies derived from in vitroprocessing of a defined population of starting cells in a definedfield of view (FOV), and (b) standardized protocols for imageprocessing and analysis.1.4 The relevant FOV may be two-dimensional or three-dimensional, depending o

5、n the CFU assay system beinginterrogated.1.5 The primary unit to be used in the outcome of analysisis the number of colonies present in the FOV. In addition, thecharacteristics and sub-classification of individual colonies andcells within the FOV may also be evaluated, based on extantmorphological f

6、eatures, distributional properties, or propertieselicited using secondary markers (for example, staining orlabeling methods).1.6 Imaging methods require that images of the relevantFOV be captured at sufficient resolution to enable detectionand characterization of individual cells and over a FOV that

7、 issufficient to detect, discriminate between, and characterizecolonies as complete objects for assessment.1.7 Image processing procedures applicable to two- andthree-dimensional data sets are used to identify cells orcolonies as discreet objects within the FOV. Imaging methodsmay be optimized for m

8、ultiple cell types and cell features usinganalytical tools for segmentation and clustering to definegroups of cells related to each other by proximity or morphol-ogy in a manner that is indicative of a shared lineagerelationship (that is, clonal expansion of a single founding stemcell or progenitor)

9、.1.8 The characteristics of individual colony objects (cellsper colony, cell density, cell size, cell distribution, cell hetero-geneity, cell genotype or phenotype, and the pattern, distribu-tion and intensity of expression of secondary markers) areinformative of differences in underlying biological

10、 propertiesof the clonal progeny.1.9 Under appropriately controlled experimental conditions,differences between colonies can be informative of the biologi-cal properties and underlying heterogeneity of colony foundingcells (CFUs) within a starting population.1.10 Cell and colony area/volume, number,

11、 and so forthmay be expressed as a function of cell culture area (squaremillimetres), or initial cell suspension volume (millilitres).1.11 Sequential imaging of the FOV using two or moreoptical methods may be valuable in accumulating quantitativeinformation regarding individual cells or colony objec

12、ts in thesample. In addition, repeated imaging of the same sample willbe necessary in the setting of process tracking and validation.Therefore, this test method requires a means of reproducibleidentification of the location of cells and colonies (centroids)within the FOV area/volume using a defined

13、coordinate sys-tem.1.12 To achieve a sufficiently large field-of-view (FOV),images of sufficient resolution may be captured as multipleimage fields/tiles at high magnification and then combinedtogether to form a mosaic representing the entire cell culturearea.1.13 Cells and tissues commonly used in

14、tissue engineering,regenerative medicine, and cellular therapy are routinely as-sayed and analyzed to define the number, prevalence, biologi-cal features, and biological potential of the original stem celland progenitor population(s).1.13.1 Common applicable cell types and cell sourcesinclude, but a

15、re not limited to: mammalian stem and progenitorcells; adult-derived cells (for example, blood, bone marrow,skin, fat, muscle, mucosa) cells, fetal-derived cells (for ex-ample, cord blood, placental/cord, amniotic fluid); embryonicstem cells (ESC) (that is, derived from inner cell mass ofblastocysts

16、); induced pluripotency cells (iPS) (for example,reprogrammed adult cells); culture expanded cells; and termi-nally differentiated cells of a specific type of tissue.1This test method is under the jurisdiction of ASTM Committee F04 on Medicaland Surgical Materials and Devices and is the direct respo

17、nsibility of SubcommitteeF04.43 on Cells and Tissue Engineered Constructs for TEMPs.Current edition approved March 1, 2012. Published April 2012. DOI: 10.1520/F294412.1Copyright ASTM International, 100 Barr Harbor Drive, PO Box C700, West Conshohocken, PA 19428-2959, United States.1.13.2 Common appl

18、icable examples of mature differenti-ated phenotypes which are relevant to detection of differentia-tion within and among clonal colonies include: hematopoieticphenotypes (erythrocytes, lymphocytes, neutrophiles, eosino-philes, basophiles, monocytes, macrophages, and so forth),mesenchymal phenotypes

19、 (oteoblasts, chondrocytes, adipo-cytes, 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 invarious tissues can be assayed in vitro by liberating the cellsfrom the tissues using

20、 methods that preserve the viability andbiological potential of the underlying stem cell and/or progeni-tor population, and placing the tissue-derived cells in an invitro environment that results in efficient activation and prolif-eration of stem and progenitor cells as clonal colonies. The truenumb

21、er of stem cells and progenitors (true colony formingunits (tCFU) can thereby be estimated on the basis of thenumber of colony-forming units observed (observed colonyforming units (oCFU) to have formed (1-3)2(Fig. A1.1). Theprevalence of stem cells and/or progenitors can be estimated onthe basis of

22、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 ap-proach (described herein) to cell and colony enumeration hasbeen validated and found to provide superior accuracy andprecision when compared t

23、o the current “gold standard” ofmanual observer defined visual cell and colony counting undera brightfield or fluorescent microscope with or without ahemocytomer (4), reducing both intra- and inter-observervariation. Several groups have attempted to automate thisand/or similar processes in the past

24、(5, 6). Recent reportsfurther demonstrate the capability of extracting qualitative andquantitative data for colonies of various cell types at thecellular and even nuclear level (4, 7).1.16 Advances in software and hardware now broadlyenable systematic automated analytical approaches. Thisevolving te

25、chnology creates the need for general agreement onunits of measurement, nomenclature, process definitions, andanalytical interpretation as presented in this test method.1.17 Standardized methods for automated CFU analysisopen opportunities to enhance the value and utility of CFUassays in several sci

26、entific and commercial domains:1.17.1 Standardized methods for automated CFU analysisopen opportunities to advance the specificity of CFU analysismethods though optimization of generalizable protocols andquantitative metrics for specific cell types and CFU assaysystems which can be applied uniformly

27、 between disparatelaboratories.1.17.2 Standardized methods for automated CFU analysisopen opportunities to reduce the cost of colony analysis in allaspects of biological sciences by increasing throughput andreducing work flow demands.1.17.3 Standardized methods for automated CFU analysisopen opportu

28、nities to improve the sensitivity and specificity ofexperimental systems seeking to detect the effects of in vitroconditions, biological stimuli, biomaterials and in vitro pro-cessing steps on the attachment, migration, proliferation, dif-ferentiation, and survival of stem cells and progenitors.1.18

29、 Limitations are described as follows:1.18.1 Colony IdentificationCell Source/Colony Type/Marker VariabilityStem cells and progenitors from varioustissue sources and in different in vitro environments willmanifest different biological features. Therefore, the specificmeans to detect cells or nuclei

30、and secondary markers utilizedand the implementation of their respective staining protocolswill differ depending on the CFU assay system, cell type(s) andmarkers being interrogated. Optimized protocols for imagecapture and image analysis to detect cells and colonies, todefine colony objects and to c

31、haracterize colony objects willvary depending on the cell source being utilized and CFUsystem being used. These protocols will require independentoptimization, characterization and validation in each applica-tion. However, once defined, these can be generalized betweenlabs and across clinical and re

32、search domains.1.18.2 Instrumentation Induced Variability in ImageCaptureChoice of image acquisition components describedabove may adversely affect segmentation of cells and subse-quent colony identification if not properly addressed. Forexample, use of a mercury bulb rather than a fiber-opticfluore

33、scent light source or the general misalignment of opticscould produce uneven illumination or vignetting of tiles imagescomprising the primary large FOV image. This may becorrected by applying background subtraction routines to eachtile in a large FOV image prior to tile stitching.1.18.3 CFU Assay Sy

34、stem Associated Variation in ImagingArtifactsIn addition to the presentation of colony objectswith unique features that must be utilized to define colonyidentification, each image from each CFU system may presentnon-cell and non-colony artifacts (for example, cell debris, lint,glass aberrations, ref

35、lections, autofluorescence, and so forth)that may confound the detection of cells and colonies if notidentified and managed.1.18.4 Image Capture Methods and Quality ControlVariationVariation in image quality will significantly affectthe precision and reproducibility of image analysis methods.Variati

36、on in focus, illumination, tile registration, exposuretime, quenching, and emission spectral bleeding, are all impor-tant potential limitations or threats to image quality andreproducibility.1.19 The values stated in SI units are to be regarded asstandard. No other units of measurement are included

37、in thisstandard.1.20 This standard does not purport to address all of thesafety concerns, if any, associated with its use. It is theresponsibility of the user of this standard to establish appro-priate safety and health practices and determine the applica-bility of regulatory limitations prior to us

38、e.2. Terminology2.1 Definitions:2.1.1 cell number, nnumber of cells counted within aculture area based upon a ubiquitous, separable cell marker(that is, nuclear stain).2The boldface numbers in parentheses refer to a list of references at the end ofthis standard.F2944 1222.1.2 colony, na cluster of c

39、ells related to each other byproximity or morphology in a manner that is indicative of ashared lineage relationship (that is, clonal expansion of a singlefounding stem cell or progenitor).2.1.3 colony area, nsum of all pixels within a givencolony multiplied by the pixel resolution (square millimetre

40、s).2.1.4 colony aspect ratio, nratio of colony major andminor axes (1 = perfect circle).2.1.5 colony centroid, ncentral pixel determined using allx- and y-coordinates of pixels within given colony (may alsobe calculated using center of best-fit ellipse or box).2.1.6 colony forming effciency (CFE), n

41、the probability ofconverting a tCFU to an oCFU, where a probability of 1.0represents 100 % conversion. Therefore the relationship be-tween tCFU to an oCFU can be defined by the relationship:tCFU 3 CFE = oCFU.2.1.7 colony major axis, nlongest dimension of the best-fit box (or ellipse) around a given

42、colony (millimetres).2.1.8 colony minor axis, nshortest dimension of the best-fit box (or ellipse) around a given colony (millimetres)2.1.9 colony or colony forming unit (CFU), na single cell,which when placed into in vitro culture will survive andproliferate to create progeny which become manifest

43、as acolony of lineage-related cells derived from the founding CFU.2.1.10 effective proliferation rate (EPR), nthe prolifera-tion rate that would be necessary to produce the number ofcells found in a given colony during the time in culture (EPR= log2(cell number)/time in days).2.1.11 observed CFU (oC

44、FU), nthe number of cells in agiven sample that form a colony of interest under the condi-tions used.2.1.12 prevalence, nnumber of colonies per cell plated(often expressed in colonies per million cells).2.1.13 proliferation rate, nthe current incidence of mito-sis within a population of cells over a

45、 defined period of time.NoteThe proliferation rate may change over time.2.1.14 secondary marker, nany marker in addition to thenuclear marker or cell localization marker that provides infor-mation related to the genotype, phenotype, biological activity,biochemical features or lineage history of a co

46、lony or cell.2.1.15 trueCFU (tCFU), nthe number of cells in a givensample that are capable of forming a colony of interest undersome optimal condition.3. Significance and Use3.1 The Manual Observer-Dependent AssayThe manualquantification of cell and CFU cultures based on observer-dependent criteria

47、or judgment is an extremely tedious andtime-consuming task and is significantly impacted by user bias.In order to maintain consistency in data acquisition, pharma-cological and drug discovery and development studies utilizingcell- and colony-based assays often require that a singleobserver count cel

48、ls and colonies in hundreds, and potentiallythousands of cultures. Due to observer fatigue, both accuracyand reproducibility of quantification suffer severely (5). Whenmultiple observers are employed, observer fatigue is reduced,but the accuracy and reproducibility of cell and colonyenumeration is s

49、till significantly compromised due to observerbias and significant intra- and inter-observer variability (4, 13).Use of quantitative automated image analysis provides data forboth the number of colonies as well as the number of cells ineach colony. These data can also be used to calculate meancells per colony. Traditional methods for quantification ofcolonies by hand counting coupled with an assay for cellnumber (for example, DNA or mitochondrial) remains a viablemethod that can be used to calculate the mean number of cellsper colony. These traditional methods