ASTM F2944-20

Standard Practice for Automated Colony Forming Unit (CFU) Assays-Image Acquisition and Analysis Method for Enumerating and Characterizing Cells and Colonies in Culture

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 ana...
SCOPE
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 ...

General Information

• Status: Published
• Publication Date: 31-Mar-2020
• ICS: 07.100.01 - Microbiology in general
• ICS: 11.100.10 - In vitro diagnostic test systems
• Technical Committee: F04 - Medical and Surgical Materials and Devices
• Drafting Committee: F04.43 - Cells and Tissue Engineered Constructs for TEMPs

Relations

• Refers: ASTM F3294-18 - Standard Guide for Performing Quantitative Fluorescence Intensity Measurements in Cell-based Assays with Widefield Epifluorescence Microscopy
• Effective Date: 01-Oct-2018

Standards Content (Sample)

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.
Designation: F2944 − 20
Standard Practice for
Automated Colony Forming Unit (CFU) Assays—Image
Acquisition and Analysis Method for Enumerating and
Characterizing Cells and Colonies in Culture
This standard is issued under the fixed designation F2944; the number immediately following the designation indicates the year of
original adoption or, in the case of revision, the year of last revision. A number in parentheses indicates the year of last reapproval. A
superscript epsilon (´) indicates an editorial change since the last revision or reapproval.
1. Scope ogy in a manner that is indicative of a shared lineage
relationship (that is, clonal expansion of a single founding stem
1.1 This practice, provided its limitations are understood,
cell or progenitor).
describes a procedure for quantitative measurement of the
number and biological characteristics of colonies derived from
1.8 The characteristics of individual colony objects (cells
a stem cell or progenitor population using image analysis.
per colony, cell density, cell size, cell distribution, cell
heterogeneity, cell genotype or phenotype, and the pattern,
1.2 This practice is applied in an in vitro laboratory setting.
distribution and intensity of expression of secondary markers)
1.3 This practice utilizes: (a) standardized protocols for
are informative of differences in underlying biological proper-
image capture of cells and colonies derived from in vitro
ties of the clonal progeny.
processing of a defined population of starting cells in a defined
1.9 Under appropriately controlled experimental conditions,
field of view (FOV), and (b) standardized protocols for image
differences between colonies can be informative of the biologi-
processing and analysis.
cal properties and underlying heterogeneity of colony founding
1.4 The relevant FOV may be two-dimensional or three-
cells (CFUs) within a starting population.
dimensional, depending on the CFU assay system being
1.10 Cell and colony area/volume, number, and so forth
interrogated.
may be expressed as a function of cell culture area (square
1.5 The primary unit to be used in the outcome of analysis
millimeters), or initial cell suspension volume (milliliters).
is the number of colonies present in the FOV. In addition, the
1.11 Sequential imaging of the FOV using two or more
characteristics and sub-classification of individual colonies and
optical methods may be valuable in accumulating quantitative
cells within the FOV may also be evaluated, based on extant
information regarding individual cells or colony objects in the
morphological features, distributional properties, or properties
sample. In addition, repeated imaging of the same sample will
elicited using secondary markers (for example, staining or
be necessary in the setting of process tracking and validation.
labeling methods).
Therefore, this practice requires a means of reproducible
1.6 Imaging methods require that images of the relevant
identification of the location of cells and colonies (centroids)
FOV be captured at sufficient resolution to enable detection
within the FOV area/volume using a defined coordinate sys-
and characterization of individual cells and over a FOV that is
tem.
sufficient to detect, discriminate between, and characterize
1.12 To achieve a sufficiently large field-of-view (FOV),
colonies as complete objects for assessment.
images of sufficient resolution may be captured as multiple
1.7 Image processing procedures applicable to two- and
image fields/tiles at high magnification and then combined
three-dimensional data sets are used to identify cells or
together to form a mosaic representing the entire cell culture
colonies as discreet objects within the FOV. Imaging methods
area.
may be optimized for multiple cell types and cell features using
analytical tools for segmentation and clustering to define
1.13 Cells and tissues commonly used in tissue engineering,
groups of cells related to each other by proximity or morphol- 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 cell
1 and progenitor population(s).
This practice is under the jurisdiction of ASTM Committee F04 on Medical and
Surgical Materials and Devices and is the direct responsibility of Subcommittee
1.13.1 Common applicable cell types and cell sources
F04.43 on Cells and Tissue Engineered Constructs for TEMPs.
include, but are not limited to: mammalian stem and progenitor
Current edition approved April 1, 2020. Published June 2020. Originally
cells; adult-derived cells (for example, blood, bone marrow,
approved in 2012. Last previous edition approved in 2012 as F2944–12. DOI:
10.1520/F2944–20. skin, fat, muscle, mucosa) cells, fetal-derived cells (for
Copyright © ASTM International, 100 Barr Harbor Drive, PO Box C700, West Conshohocken, PA 19428-2959. United States
F2944 − 20
example, cord blood, placental/cord, amniotic fluid); embry- quantitative metrics for specific cell types and CFU assay
onic stem cells (ESC) (that is, derived from inner cell mass of systems which can be applied uniformly between disparate
blastocysts); induced pluripotent cells (iPC) (for example, laboratories.
reprogrammed adult cells); culture expanded cells; and termi-
1.17.2 Standardized methods for automated CFU analysis
nally differentiated cells of a specific type of tissue.
open opportunities to reduce the cost of colony analysis in all
1.13.2 Common applicable examples of mature differenti-
aspects of biological sciences by increasing throughput and
ated phenotypes which are relevant to detection of differentia-
reducing work flow demands.
tion within and among clonal colonies include: hematopoietic
1.17.3 Standardized methods for automated CFU analysis
phenotypes (erythrocytes, lymphocytes, neutrophiles,
open opportunities to improve the sensitivity and specificity of
eosinophiles, basophiles, monocytes, macrophages, and so
experimental systems seeking to detect the effects of in vitro
forth), adult tissue-specific progenitor cell phenotypes
conditions, biological stimuli, biomaterials and in vitro pro-
(oteoblasts, chondrocytes, adipocytes, and so forth), and other
cessing steps on the attachment, migration, proliferation,
tissues (hepatocytes, neurons, endothelial cells, keratinocyte,
differentiation, and survival of stem cells and progenitors.
pancreatic islets, and so forth).
1.18 Limitations are described as follows:
1.14 The number of stem cells and progenitor cells in
1.18.1 Colony Identification—Cell Source/Colony Type/
various tissues can be assayed in vitro by liberating the cells
Marker Variability—Stem cells and progenitors from various
from the tissues using methods that preserve the viability and
tissue sources and in different in vitro environments will
biological potential of the underlying stem cell and/or progeni-
manifest different biological features. Therefore, the specific
tor population, and placing the tissue-derived cells in an in
means to detect cells or nuclei and secondary markers utilized
vitro environment that results in efficient activation and prolif-
and the implementation of their respective staining protocols
eration of stem and progenitor cells as clonal colonies. The true
will differ depending on the CFU assay system, cell type(s) and
number of stem cells and progenitors (true colony forming
markers being interrogated. Optimized protocols for image
units (tCFU)) can thereby be estimated on the basis of the
capture and image analysis to detect cells and colonies, to
number of colony-forming units observed (observed colony
define colony objects and to characterize colony objects will
forming units (oCFU)) to have formed (1-3) (Fig. A1.1). The
vary depending on the cell source being utilized and CFU
prevalence of stem cells and/or progenitors can be estimated on
system being used. These protocols will require independent
the basis of the number of observed colony-forming units
optimization, characterization and validation in each applica-
(oCFU) detected, divided by the number of total cells assayed.
tion. However, once defined, these can be generalized between
1.15 The automated image acquisition and analysis ap-
labs and across clinical and research domains.
proach (described herein) to cell and colony enumeration has
1.18.2 Instrumentation-Induced Variability in Image
been validated and found to provide superior accuracy and
Capture—Choice of image acquisition components described
precision when compared to the current “gold standard” of
above may adversely affect segmentation of cells and subse-
manual observer defined visual cell and colony counting under
quent colony identification if not properly addressed. For
a brightfield or fluorescent microscope with or without a
example, use of a mercury bulb rather than a fiber-optic
hemocytometer (4), reducing both intra- and inter-observer
fluorescent light source or the general misalignment of optics
variation. Several groups have attempted to automate this
could produce uneven illumination or vignetting of tiled
and/or similar processes in the past (5, 6). Recent reports
images comprising the primary large FOV image. This may be
further demonstrate the capability of extracting qualitative and
corrected by applying background subtraction routines to each
quantitative data for colonies of various cell types at the
tile in a large FOV image prior to tile stitching.
cellular and even nuclear level (4, 7).
1.18.3 CFU Assay System Associated Variation in Imaging
1.16 Advances in software and hardware now broadly
Artifacts—In addition to the presentation of colony objects
enable systematic automated analytical approaches. This
with unique features that must be utilized to define colony
evolving technology creates the need for general agreement on
identification, each image from each CFU system may present
units of measurement, nomenclature, process definitions, and
non-cell and non-colony artifacts (for example, cell debris, lint,
analytical interpretation as presented in this practice.
glass aberrations, reflections, autofluorescence, and so forth)
that may confound the detection of cells and colonies if not
1.17 Standardized methods for automated CFU analysis
open opportunities to enhance the value and utility of CFU identified and managed.
assays in several scientific and commercial domains:
1.18.4 Image Capture Methods and Quality Control
1.17.1 Standardized methods for automated CFU analysis
Variation—Variation in image quality will significantly affect
open opportunities to advance the specificity of CFU analysis
the precision and reproducibility of image analysis methods.
methods though optimization of generalizable protocols and
Variation in focus, illumination, tile registration, exposure
time, quenching, and emission spectral bleeding, are all impor-
2 tant potential limitations or threats to image quality and
The boldface numbers in parentheses refer to a list of references at the end of
this standard. reproducibility.
F2944 − 20
1.19 The values stated in SI units are to be regarded as 3.1.8 effective proliferation rate (EPR), n— proliferation
standard. No other units of measurement are included in this rate that would be necessary to produce the number of cells
standard. found in a given colony during the time in culture (EPR =
log (cell number)/time in days).
1.20 This standard does not purport to address all of the
safety concerns, if any, associated with its use. It is the 3.1.9 observed CFU (oCFU), n—number of cells in a given
responsibility of the user of this standard to establish appro- sample that form a colony of interest under the conditions used.
priate safety, health, and environmental practices and deter-
3.1.10 prevalence, n—number of colonies per cell plated
mine the applicability of regulatory limitations prior to use.
(often expressed in colonies per million cells).
1.21 This international standard was developed in accor-
3.1.11 proliferation rate, n—current incidence of mitosis
dance with internationally recognized principles on standard-
within a population of cells over a defined period of time.
ization established in the Decision on Principles for the
Development of International Standards, Guides and Recom- NOTE 1—The proliferation rate may change over time.
mendations issued by the World Trade Organization Technical
3.1.12 secondary marker, n—any marker in addition to the
Barriers to Trade (TBT) Committee.
nuclear marker or cell localization marker that provides infor-
mation related to the genotype, phenotype, biological activity,
2. Referenced Documents
biochemical features or lineage history of a colony or cell.
2.1 ASTM Standards:
3.1.13 trueCFU (tCFU), n—number of cells in a given
F2998 Guide for Using Fluorescence Microscopy to Quan-
sample that are capable of forming a colony of interest under
tify the Spread Area of Fixed Cells
some optimal condition.
F3294 Guide for Performing Quantitative Fluorescence In-
3.2 Definitions of Terms Specific to This Standard:
tensity Measurements in Cell-based Assays with Wide-
3.2.1 colony, n—cluster of cells related to each other by
field Epifluorescence Microscopy
proximity or morphology in a manner that is indicative of a
2.2 ISO Standards:
shared lineage relationship (that is, clonal expansion of a single
ISO 20391-1 Biotechnology— Cell Counting—Part 1: Gen-
founding stem cell or progenitor).
eral Guidance on Cell Counting Methods
ISO 20391-2 Biotechnology—Cell Counting—Part 2: Ex-
3.2.2 colony forming unit (CFU), n—single cell, which
perimental Design and Statistical Analysis to Quantify
when placed into appropriate in vitro culture conditions will
Counting Method Performance
survive and proliferate to create progeny that become manifest
as a colony of lineage-related cells derived from the founding
3. Terminology
cell.
3.1 Definitions:
3.1.1 cell number, n—number of cells counted within a
4. Significance and Use
culture area based upon a ubiquitous, separable cell marker
4.1 The Manual Observer-Dependent Assay—The manual
(that is, nuclear stain).
quantification of cell and CFU cultures based on observer-
3.1.2 colony area, n—sum of all pixels within a given
dependent criteria or judgment is an extremely tedious and
colony multiplied by the pixel resolution (square millimeters).
time-consuming task and is significantly impacted by user bias.
3.1.3 colony aspect ratio, n—ratio of colony major and In order to maintain consistency in data acquisition, pharma-
minor axes (1 = perfect circle).
cological and drug discovery and development studies utilizing
cell- and colony-based assays often require that a single
3.1.4 colony centroid, n—central pixel determined using all
observer count cells and colonies in hundreds, and potentially
x- and y-coordinates of pixels within a given colony (may also
thousands of cultures. Due to observer fatigue, both accuracy
be calculated using center of best-fit ellipse or box).
and reproducibility of quantification suffer severely (5). When
3.1.5 colony forming effıciency (CFE), n— probability of
multiple observers are employed, observer fatigue is reduced,
converting a tCFU to an oCFU, where a probability of 1.0
but the accuracy and reproducibility of cell and colony
represents 100 % conversion. Therefore the relationship be-
enumeration is still significantly compromised due to observer
tween tCFU to an oCFU can be defined by the relationship:
bias and significant intra- and inter-observer variability (2, 4).
tCFU × CFE = oCFU.
Use of quantitative automated image analysis provides data for
3.1.6 colony major axis, n—longest dimension of the best-fit
both the number of colonies as well as the number of cells in
box (or ellipse) around a given colony (millimeters).
each colony. These data can also be used to calculate mean
3.1.7 colony minor axis, n—shortest dimension of the best- cells per colony. Traditional methods for quantification of
fit box (or ellipse) around a given colony (millimeters) 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
For referenced ASTM standards, visit the ASTM website, www.astm.org, or
per colony. These traditional methods have the advantage that
contact ASTM Customer Service at [e-mail]. For Annual Book of ASTM
Standards volume information, refer to the standard’s Document Summary page on
they are currently less labor intensive and less technically
the ASTM website.
demanding (8, 9). However, the traditional assays do not,
Available from International Organization for Standardization (ISO), ISO
provide colony level information (for example, variation and
Central Secretariat, BIBC II, Chemin de Blandonnet 8, CP 401, 1214 Vernier,
Geneva, Switzerland, http://www.iso.org. skew), nor do they provide a means for excluding cells that are
F2944 − 20
not part of a colony from the calculation of mean colony size. effective proliferation rates, and so forth (Fig. A1.2). In
As a result, the measurement of the mean number of cells per addition to human connective tissue progenitors (CTPs) de-
colony that is obtained from these alternative methods may rived from bone, bone marrow, cartilage, adipose tissue,
differ when substantial numbers of cells in a sample are not muscle, periosteum, and synovium, this practice and technol-
associated with colony formation. By employing state-of-the- ogy has been implemented in the cell and colony identification
art image acquisition, processing and analysis hardware and and characterization of several cell and tissue types including:
software, an accurate, precise, robust and automated analysis umbilical cord blood hematopoietic stem cells (Fig. X1.2);
system is realized. adipose-derived stem cells (Fig. X1.3); and human epidermal
(Fig. X1.4) and dermal (Fig. X1.5) stem cells.
4.1.1 Areas of Application—Cell and colony enumeration
(CFU assay) is becoming particularly important in the
4.3 Benefits of Automated Analysis of CFU Assays—
manufacture, quality assurance/control (QA/QC), and develop-
Automated analysis is expected to provide more rapid,
ment of product safety and potency release criteria for cell-
reproducible, and precise results in comparison to the manual
based regenerative medicine and cellular therapy. The U.S.
enumeration of cells and colonies utilizing a microscope and
Food and Drug Administration (FDA) has a guidance docu-
hemocytometer. In addition to being time consuming, labor
ment that indicates that the CFU assay may be appropriate for
intensive, and subjective, manual enumeration has been shown
testing stability of placental and umbilical cord blood-derived
to have a significant degree of intra- and inter-observer
stem cells (7). Since cell source validation and QA/QC
variability, with coefficients of variation (CV) ranging from 8.1
comprise approximately 50 % of the manufacturing cost of
% to 40.0 % and 22.7 % to 80 %, respectively. Standard CVs
cellular therapies (10), developing a precise, robust, and
for cell viability assessment and progenitor (colony) type
cost-effective means for enumerating cells and colonies is vital
enumeration have been shown to range from 19.4 % to 42.9 %
to sustainability and growth in this industry. The broad areas of
and 46.6 % to 100 %, respectively (4, 11, 12). In contrast,
use for automated analysis of colony forming unit assays
studies focusing on bacteria, bone marrow-derived stem cells
include:
and osteogenic progenitor cells have collectively concluded
4.1.1.1 Characterization of a cell source by correlating
that automated enumeration provides significantly greater
biological potential and functional potency with CFU forma-
accuracy, precision, and/or speed for counting and sizing cells
tion.
and colonies, relative to conventional manual methodologies
4.1.1.2 Characterization of the effect of processing steps or
(4-6). Automated methods for enumerating cells and colonies
biological or physical manipulation (for example, stimuli) on
are less biased, less time consuming, less laborious, and
cells or colony formation.
provide greater qualitative and quantitative data for intrinsic
4.1.1.3 Cell and colony characterization using specific fluo-
characteristics of cell and colony type and morphology.
rescent and non-fluorescent (differentiation) markers.
4.4 Selection of Cell Culture Surface Area and Optimal Cell
4.1.1.4 Extrapolation of the biological potency (for
Seeding Density—When performing a CFU assay, optimizing
example, differentiation, proliferative, and so forth) of a larger
the cell culture surface area and cell seeding density is critical
sample from application of colony forming assay to sub-
to developing methods for generating reliable and reproducible
samples.
colony- and cell-level data. If seeding density is too low, then
4.1.1.5 Provision of criteria for sub-colony selection of
the frequency of observed colonies is decreased. This can result
preferred colonies (specific tissue type, proliferation rate, and
in a sampling size that is inadequate to characterize the
so forth) for use and/or further expansion.
population of CFUs in the sample. If seeding density is too
4.2 The Technology (image acquisition, processing, and high, the colonies that are formed may be too closely spaced.
analysis)—Current standards utilize user input for defining the
Overlapping colony footprints compromise colony counting
presence and location of colonies based on visualization of an and characterization. Because the intrinsic range of CFU
entire culture surface at low magnification through the eye- prevalence in a given cell source may vary widely, in many
pieces of a microscope. In this case, the sample may be viewed cases, a trial and error approach to optimizing cell seeding
in transmission light mode (unstained or with a histochemical density (or range of densities) that are needed for a given cell
marker) or fluorescently with a dye or antibody. For this source will be necessary. It is important to note that the more
practice, the colo
...

This document is not an ASTM standard and is intended only to provide the user of an ASTM standard an indication of what changes have been made to the previous version. Because
it may not be technically possible to adequately depict all changes accurately, ASTM recommends that users consult prior editions as appropriate. In all cases only the current version
of the standard as published by ASTM is to be considered the official document.
Designation: F2944 − 12 F2944 − 20
Standard Test Method Practice for
Automated Colony Forming Unit (CFU) Assays—Image
Acquisition and Analysis Method for Enumerating and
Characterizing Cells and Colonies in Culture
This standard is issued under the fixed designation F2944; the number immediately following the designation indicates the year of
original adoption or, in the case of revision, the year of last revision. A number in parentheses indicates the year of last reapproval. A
superscript epsilon (´) indicates an editorial change since the last revision or reapproval.
1. Scope
1.1 This test method, 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 test method is applied in an in vitro laboratory setting.
1.3 This method 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 millimetres),
or initial cell suspension volume (millilitres).
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 test method 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.
This test method practice is under the jurisdiction of ASTM Committee F04 on Medical and Surgical Materials and Devices and is the direct responsibility of
Subcommittee F04.43 on Cells and Tissue Engineered Constructs for TEMPs.
Current edition approved March 1, 2012April 1, 2020. Published April 2012June 2020. Originally approved in 2012. Last previous edition approved in 2012 as F2944–12.
DOI: 10.1520/F2944–12.10.1520/F2944–20.
Copyright © ASTM International, 100 Barr Harbor Drive, PO Box C700, West Conshohocken, PA 19428-2959. United States
F2944 − 20
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
pluripotency cells (iPS) (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), mesenchymal 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) (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 hemocytomer (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 test method.
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 tiles 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.
The boldface numbers in parentheses refer to a list of references at the end of this standard.
F2944 − 20
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.
2. Terminology
2.1 Definitions:
2.1.1 cell number, n—number of cells counted within a culture area based upon a ubiquitous, separable cell marker (that is,
nuclear stain).
2.1.2 colony, n—a cluster 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).
2.1.3 colony area, n—sum of all pixels within a given colony multiplied by the pixel resolution (square millimetres).
2.1.4 colony aspect ratio, n—ratio of colony major and minor axes (1 = perfect circle).
2.1.5 colony centroid, n—central pixel determined using all x- and y-coordinates of pixels within given colony (may also be
calculated using center of best-fit ellipse or box).
2.1.6 colony forming effıciency (CFE), n—the probability of converting a tCFU to an oCFU, where a probability of 1.0
represents 100 % conversion. Therefore the relationship between tCFU to an oCFU can be defined by the relationship: tCFU × CFE
= oCFU.
2.1.7 colony major axis, n—longest dimension of the best-fit box (or ellipse) around a given colony (millimetres).
2.1.8 colony minor axis, n—shortest dimension of the best-fit box (or ellipse) around a given colony (millimetres)
2.1.9 colony or colony forming unit (CFU), n—a single cell, which when placed into in vitro culture will survive and proliferate
to create progeny which become manifest as a colony of lineage-related cells derived from the founding CFU.
2.1.10 effective proliferation rate (EPR), n—the proliferation rate that would be necessary to produce the number of cells found
in a given colony during the time in culture (EPR = log (cell number)/time in days).
2.1.11 observed CFU (oCFU), n—the number of cells in a given sample that form a colony of interest under the conditions used.
2.1.12 prevalence, n—number of colonies per cell plated (often expressed in colonies per million cells).
2.1.13 proliferation rate, n—the current incidence of mitosis within a population of cells over a defined period of time.
Note—The proliferation rate may change over time.
2.1.14 secondary marker, n—any marker in addition to the nuclear marker or cell localization marker that provides information
related to the genotype, phenotype, biological activity, biochemical features or lineage history of a colony or cell.
2.1.15 trueCFU (tCFU), n—the number of cells in a given sample that are capable of forming a colony of interest under some
optimal condition.
3. Significance and Use
3.1 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 (4, 8). 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
(9, 10). 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.
F2944 − 20
3.1.1 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. In 2006, the Food and Drug Administration (FDA) released a guidance document foreshadowing
the importance of the colony assay in establishing an audit trail for the aforementioned steps of product development in this
industry (11). Corroborating this document, the FDA released an additional guidance document in 2009 proposing the potential
significance of the colony assay with regard to functional potency of placental and umbilical cord blood-derived stem cells in
regenerative medicine and therapeutic applications (12). Since cell source validation and QA/QC comprise approximately 50 %
of the manufacturing cost of cellular therapies (13), 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:
3.1.1.1 Characterization of a cell source by correlating biological potential and functional potency with CFU formation.
3.1.1.2 Characterization of the effect of processing steps or biological or physical manipulation (for example, stimuli) on cells
or colony formation.
3.1.1.3 Cell and colony characterization using specific fluorescent and non-fluorescent (differentiation) markers.
3.1.1.4 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.
3.1.1.5 Provision of criteria for sub-colony selection of preferred colonies (specific tissue type, proliferation rate, and so forth)
for use and/or further expansion.
3.2 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 test method, 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), this test method and technology has been implemented in the cell and
colony identification and characterization of several cell and tissue types including: Cartilage Progenitor Cells (Fig. X1.1);
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.
3.3 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, hemocytometer, and so forth.
In addition to being highly time and 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, 8, 14). 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.
3.4 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 (250 000 cell per 1 mL of cell culture medium) and a cell culture surface area of 22 by 22 mm (dual-chamber Lab-Tek
culture slides) (15, 16).
F2944 − 20
4. Interferences
4.1 Nuclear Aggregation—Depending upon the specific implementation of morphologically-based filters utilized to separate
cells stained with a particular nuclear marker (that is, watershed), there is a possibility of over- or under-estimation of cell number
that could potentially affect cell clustering for colony identification.
4.2 Localization of Secondary Markers—While various processing filters may be utilized to separate distinct, regular shaped
objects such as nuclei, cellular bodies visualized via cytoplasmic markers are generally amorphous in size and shape posing
problems for accurate separation of cells in contact. Thus, while the staining area of these markers may be determined for a
particular colony or culture surface area, the amount of staining cannot always be determined for a specific cell.
4.3 Cellular and Non-cellular Debris—In general debris (non-cellular or apoptotic), may be segmented using morphological-
based criteria (that is, area, length, aspect ratio, and so forth) or intensity (that is, lack of gradient, gray-level intensity, and so forth)
and subtracted from the large FOV image of the cell culture surface prior to nuclear clustering. Debris that is consistent with the
size and shape of nuclei may not be easily removed unless visible in another portion of the fluorescence spectrum where neither
the nuclear marker nor secondary markers are present.
4.4 Colony Forming Effıciency—While a frequent use for the standard method proposed is to determine the true number of
CFUs in a given sample or cell source, it must be recognized that the number of true CFUs (tCFU) will only be the same as the
observed number of CFUs (oCFU) if all potential CFUs form colonies under conditions of the assay. This is only true if the
efficiency of converting a tCFU to an oCFU is 1.0 or 100 %. Therefore, the relationship between a tCFU to an oCFU is dependent
upon the colony forming efficiency (CFE) of the assay conditions used and can be represented by the relationship: tCFU × CFE
= oCFU. In any given cell source, variables that influence CFE can be systematically explored by culturing identical samples under
varying conditions. Using this experimental design, since tCFU in the starting samples is constant; changes in oCFU are the result
of changes in CFE.
5. Apparatus
5.1 Imaging Requirements—In the setting of image capture using the strategy described in 1.12, the components of
instrumentation necessary for generating large FOV images may include a microscope stand with 5 to 40× magnification objective
lenses, a fluorescence filter turret with basic filter cube sets, a fluorescence energy source (mercury, xenon, and so forth), an x-,
y -, z- motorized (linearly-encoded) stage, a charge-coupled device (CCD) camera, and imaging software to acquire, process, and
save images acquired by the CCD camera. Each of these components is available from multiple commercial vendors.
5.2 Computational Requirements—Image processing steps (that is, clustering) require access to the entire, contiguous pixel
dataset stored in each high resolution, large FOV image, short-term random-access memory (RAM) (>2 gigabytes (GB)) and
long-term memory storage (>200 GB) is needed. This requirement will vary depending on the area or volume of the FOV required
and on the resolution (pixel or voxel size) required for the individual application.
6. Hazards
6.1 Warning (Electrical)—High voltages are present inside this instrument. Instrument shall be sited on a firm, dry work space
and be properly grounded.
6.2 Warning (Biological)—Institutional-, state-, and Occupational, Safety and Health Administration (OSHA)-approved safety
action plans shall be followed.
7. Procedure
7.1 Tile images are acquired by raster scanning across a cell culture surface using a motorized microscope fitted with multiple
fluorescence filters and light source, x-, y-, z-motorized scanning stage, high magnification objective lenses (may range from 5 to
40×), and a CCD camera (for example, monochrome, quantum efficiency of 55 % @ 500 nm, 7 by 7 μm pixel size, and 1600 by
1200 resolution).
7.1.1 For cell cultures forming three-dimensional colonies (that is, cells in matrix such as methyl cellulose), z-axis image slices
(predefined range where the number of slices correlates with slice thickness and the depth of the colony) are also acquired at each
tile’s x,y position.
7.1.2 Scanning shall be performed for at least one nuclear marker and repeated for other secondary markers. Image resolution
should be high enough (0.7 to 2.8 μm) to provide at least 40 pixels for each nucleus.
7.2 To clean and prepare the image tiles for stitching and subsequent analysis, uneven illumination and vignetting in each
scanned tile image shall be background corrected (a single black/blank tile image is smoothed and divide into every image tile)
and flattened (removal of any uneven illumination from light source).
7.3 Background corrected and flattened tile images are stitched together into a single, large field-of-view (FOV) image.
7.4 Individual cell nuclei are segmented using a predefined global threshold, local neighborhood examination of pixel(s)
intensity, or spectral filters.
F2944 − 20
7.5 Objects that do not fall within the predefined area range for the nuclei morphology (for example, size, shape, and so forth
for a given cell type – human nuclei = 18.7 μm ) of a given cell type shall be rejected and digitally removed from the large FOV
image.
7.6 Objects that are not consistent with the general elliptical shape of a nucleus (that is, aspect ratio of >3.0) shall be rejected
and digitally removed from the large FOV image.
7.7 Objects that do not exhibit a radial pixel intensity gradient that increases toward their respective centroids shall be rejected
and digitally removed from the large FOV image (removal of object with uniform pixel intensity, for example, lint or apoptotic
debris).
7.8 The remaining objects, cell nuclei, shall be clustered/grouped to define larger scale colony objects using a predefined
criteria. (For example, at least eight cells whose nuclei are separated by less than or equal to 5 μm.)
7.9 Colony outlines (border or perimeter of the object) and masks (entire “footprint” of an object, for example, area within the
object outline) are generated.
7.10 Colony masks are applied/multiplied to secondary marker images.
7.11 Global thresholds shall be used to define the percentage coverage (that is, overlap) of these markers for each cell and/or
colony based on the amount of overlap/correlation between the colony and secondary marker masks.
7.12 The resulting qualitative and quantitative parameters are output for subsequent organization and analysis using graphing
and statistical software.
7.12.1 The qualitative and quantitative parameters include, but are not limited to:
7.12.1.1 Colony number;
7.12.1.2 Prevalence;
7.12.1.3 Number of cells for a given colony;
7.12.1.4 Total cell number (within and not within a colony);
7.12.1.5 Effective proliferation rate;
7.12.1.6 Cell density per colony;
7.12.1.7 Percent expression of a given differentiation marker per colony;
7.12.1.8 Number of cells positive/negative for a given differentiation marker (for example, per colony or per area of culture
surface).
8. Precision and Bias
8.1 The precision and bias using this test method are expected to vary depending on the quality of the images used for analysis.
The quality of the images will be dependent upon the quantum efficiency and resolution of the camera used, numerical aperture
of the objectives used, and the emission and excitation profiles of the filters used. Precision and bias will also be dependent upon
the cell type being analyzed (for example, cell morphology, growth pattern) and the sensitivity and specificity of the biological
markers which are utilized to identify the cells and cellular features.
8.2 Improved precision and reduced bias have been empirically demonstrated in the case of colony and cell identification and
classification in human bone marrow-derived connective tissue progenitors (CTPs). The automated method for colony
identification agreed with multiple skilled observers on individual colonies over 85 % of the time. Moreover, concordance in
colony and cell identification between individual skilled observers and the automated system was 2.7 times greater than
concordance between the skilled observers. Colony numbers obtained using this automated method demonstrated a correlation
coefficient of 0.99 with data from skilled observers.
8.3 Precision and reproducibility between image acquisition systems has been characterized in the analysis of human bone
marrow-derived CTP colony formation. Multiple large field-of-view (FOV) motorized microscopes were used to assess intra- and
inter-hardware precision and reproducibility. A detailed description of the experimental approach and results will be appended here
upon availability.
9. Keywords
9.1 automated cell and colony enumeration; automated colony forming unit assays; biological potential; cells; cell-based assays;
cell source validation; cell therapies; CFU; colonies; colony area; colony cell density; colony morphology; colony size; counting;
differentiation markers; drug developments; drug discoveries; FDA; image acquisition; image analysis; image processing;
pharmaceuticals; progenitor cells; proliferation rates; quality control; quality assurance; QA/QC; stem cells
F2944 − 20
ANNEX
(Mandatory Information)
A1. SAMPLE IMAGE ANALYSIS PROCEDURE
A1.1 See Fig. A1.1 and Fig. A1.2.
NOTE 1—Colonies are displayed on tissue culture plastic and are
manually enumerated by hand counting by multiple observers.
FIG. A1.1 Traditional CFU Assay for Manual Analysis by Multiple
Observers
F2944 − 20
NOTE 1—(A) Large FOXFOV images (480 image tiles stitched together) of Connective Tissue Progenitors (CTPs) seeded on 22 mm by 22 mm chamber
slides and stained with 4’,6–diamidino-2–phenylindole (DAPI). The images shown here have been background corrected (flattened illumination for each
tile) and processed for the removal of artifacts. (B) Images processed for colony segmentation (red outline) using various automated algorithms. (C)
Magnified colony (delineated with yellow box in B indicating various quantitative parameters that may be extracted.
FIG. A1.2 Automated CFU Analysis of Primary Adult Stem Cell from Bone Marrow
APPENDIX
(Nonmandatory Information)
X1. ILLUSTRATIVE EXAMPLES OF QUANTITATIVE IMAGE ANALYSIS APPLIED TO THE CHARACTERIZATION OF
VARIOUS CELL POPULATIONS
X1.1 See Figs. X1.1-X1.5.
F2944 − 20
NOTE 1—Analysis of Chondrogenesis in 2D Colony Assay. Cartilege-derivedCartilage-derived CTPs were cultured on 2 cm by 2 cm Lab-tek glass slides
under chondrogenic conditions for six days. At day six, cells were stained in situ with acridine orange (AO) and counterstained with DAPI. Large FOV
imaging and quantitative image analysis software was used to identify, count, and analyze colonies. Outcome parameters include (but are not limited to)
cells per colony (N ), colony area (A), cell density (D) (cells per area), area fraction of AO staining (AF ). As we have found in each tissue we have
C AO
assessed, colonies varied significantly with respect to each of these paramters.parameters. For example, AF ranged from 5 % to over 80 %.
AO
F2944 − 20
FIG. X1.1 Automated CFU Analysis of Cartilege-derivedCartilage-Derived Connective Tissue Progenitors
F2944 − 20
NOTE 1—CFU Analysis of Human Umbilical Cord Blood Stem Cells.analysis of human umbilical cord blood stem cells. Cells were cultured in
methylcellulose at 20 % oxygen for 10 to 14 days. Colony subtypes were defined by colony color and morphology as defined by trained Cord Bloodcord
blood bank technicians. Colony-level metrics included colony subtype (BFU-E, CFU-GM, or (blast forming unit – erythrocyte, BFU-E,; colony forming
unit – granulocyte macrophage, CFU-GM,; or colony forming unit – granulocyte erythrocyte macrophage megakaryocyte, CFU-GEMM) and total number
of each colony subtype. (A) Large field-of-view image of a 35 mm cell culture dish (120 image tiles). (B) Background corrected montage image. (C)
Spectrally enhanced image. (D) Thresholds applied. (E) Grid line, bubble, and debris segmentation and removal. (F) Colony segmentation and
characterization. (G) Overlay of distinct colony subtypes (BFU-E, red, CFU-GM, blue). (H) Magnified view of green Region of Interest from G.
F2944 − 20
FIG. X1.2 Automated CFU Analysis of Hematopoietic Stem Cells from Cord Blood
F2944 − 20
NOTE 1—Large field of view (FOV) images of colonies grown on Lab-tek chamber slides under osteogenic conditions, each showing the entire 2 cm by
2 cm chamber. Colony outlines are shown in black. Areas of alkaline phosphatase (AP) expression are shown in red. In general, compared to BM-derived
bone marrow derived CTPs (BM), fat-derived CTPs (Fat) form colonies with slower proliferation that migrate over larger areas at lower cell density, and
express far less AP per colony. However, heterogeneity is seen in both BMBM- and Fat-derivedfat-derived cell populations.
F2944 − 20
FIG. X1.3 Automated CFU Analysis of Adiopose-derivedAdipose-Derived Stem Cells
F2944 − 20
NOTE 1—Human Epidermal Melanocytes (HEM) and Keratinocytes (HEK) co-cultureco-cultured at 1:10 HEM:HEK dilution. HEK and HEM colonies
were defined by their unique fluorescent markers and cell morphology. Fluorescent and phase contrast microscopy images were both utilized to aid in
colony identification and characterization. Metrics extracted included colony subtypes, number of cells per colony, colony area, and colony location (x,y
centroid coordinates). (A) Large FOV, fluorescence image (20×, 0.35 μm/pixle)μm/pixel) of HEMs labeled with MEL5 (green) and HEKs labeled withfor
cytokeratin (red). (B) Corresponding phase-contrast image indicating fully-automated delineation of HEKs (red outlines) and HEMs (green outlines) using
fluorescence-based processing and segmentation. (C, D) Magnified representations of regions indicated in A and B. Note: Automated segmentation of
these cell types may be possibly using only phase-contrast-based morphometric analysis in conjunction with improved optical hardware.
F2944 − 20
FIG. X1.4 Automated CFU Analysis of Epidural Stem and Progenitor Cells
F2944 − 20
NOTE 1—Co-culture of Human Dermal Fibroblastshuman dermal fibroblasts (HDF) and Microvascular Endothelial Cellsmicrovascular endothelial cells
(HDMEC). HDF and HDMEC colonies were defined by their unique fluorescent markers, cell morphology, and pattern of colony formation. For example,
HDMEC colonies tend to grow within confluent fields of HDF cells. Fluorescent and phase contrast microscopy images were both utilized to aid in colony
identification and characterization. Metrics extracted included colony subtype, number of colony subtypes, number of cells per colony, colony area,
nuclear density, and colony location (x,y centroid coordinates). (A) Phase-contrast large FOV image (20×, 0.35 μm/pixel) image. (B) Fully-automated
delineation of HDMEC (red outlines) using phase-contrast image segmentation and Euclidian distance map-based clustering. (C) Corresponding
fluorescence image (HDFs labeled with fibroblast surface protein—red; HDMECs labeled with CD31—green). (D) Fully automated delineating of
HDMEC (yellow outlines) using a multi-step clustering algorithm that utilizes both cell markers. Note the general agreement between outlines in B and
D.
F2944 − 20
FIG. X1.5 Automated CFU Analysis of Dermal Stem and Progenitor Cells
REFERENCES
(1) Muschler, G.F., Nakamoto, C., Griffith, L.G., “Engineering Principles of Clinical Cell-Based Tissue Engineering,” Journal of Bone and Joint Surgery,
Vol 86-A, No. 7, 2004, pp. 1541–1558.
(2) Owen M., Friedenstein,A.J.,“Stromal Stem Cells: Marrow-derived Osteogenic Precursors,” Ciba Found Symp., Vol. 136, 1988, pp. 42–60.
(3) Friedenstein, A.J., Petrakova, K.V., Kurolesova, A.I., Frolova,G.P.,“Heterotopic of Bone Marrow. Analysis of Precursor Cells for Osteogenic and
Hematopoietic Tissues,” Transplantation, Vol. 6, 1968, pp. 230–247.
(4) Powell, K.A., et al., “Quantitative Image Analysis of Connective Tissue Progenitors,” Analytical and Quantitative Cytology and Histology, Vol 29,
No. 2, 2007, pp. 112–120.
(5) Goss, W.A., Michaud, R.N., McGrath, M.B., “Evaluation of an Automated Colony Counter,” Applied Microbiology, Vol 27, No. 1, 1974, pp. 264–267.
(6) Bidula, J., et al., “Osteogenic progenitors in bone marrow aspirates from smokers and nonsmokers,” Clin. Orthop. Relat. Res., Vol 442, 2006, pp.
252–259.
(7) Food and Drug Administration, “Minimally Manipulated, Unrelated, Allogeneic Placental/Umbilical Cord Blood Intended for Hematopoietic
Reconstitution in Patients with Hematological Malignancies,” Draft Guidance for Industry Document, 2006. (www.fda.gov/cber/gdlns/cordbld.htm)
(8) Clarke, E., “Standardization Tools and Instructional Materials for Hematopoietic Colony Assay,” Global Proficiency Testing Program: Stem Cell
Technologies, Vol 1.0.0, pp. 1–2.
(9) Gallagher, S. R., Quantitation of DNA and RNA with absorption and fluorescence spectroscopy, CurrProtocNeurosci, 2011 Jul; Appendix 1:
Appendix 1K, PubMed PMID: 21732311.
(10) Gallagher S. R., Quantitation of DNA and RNA with absorption and fluorescence spectroscopy, Curr. Protoc. Mol. Biol, 2011 Jan; Appendix 3: 3D,
PubMed PMID: 21225635.
(11) Food and Drug Administration, “Minimally Manipulated, Unrelated, Allogeneic Placental/Umbilical Cord Blood Intended for Hematopoietic
Reconstitution for Specified Indications,” Draft Guidance for Industry Document, 2009. (www.fda.gov/downloads/BiologicsBloodVaccines/
GuidanceComplianceRegulatoryInformation/Guidances/Blood/UCM187144.pdf)
(12) McAllister, T., et al., “Cell-based therapeutics from an economic perspective: primed for a commercial success or a research sinkhole?,”
Regenerative Medicine, Vol 3, No. 6, 2008, pp. 925–937.
(13) Lumley, M., Burgess, R., et al., “Colony counting is a major source of variation in CFU-GM results between centres,” British Journal of
Haematology, Vol 97, 1997, pp. 481–484.
(14) Muschler, G. and Midura, R. “Connective Tissue Progenitors: Practical Concepts for Clinical Applications,” Clinical Orthopedics and Related
Research, Vol 395, 2002, pp. 66–80.
(15) Muschler, G., Midura, R., and Nakamoto, C. “Practical Modeling Concepts for Connective Tissue Stem Cell and Progenitor Compartment Kinetics,”
Journal of Biomedicine and Biotechnology, Vol 3, 2003, pp. 170-193.
(16) Villarruel, S., et al., “The Effect of Oxygen Tension on the In Vitro Assay of Human Osteoblastic Connective Tissue Progenitor Cells,” Journal of
Orthopaedic Research, Vol 26, No. 10, 2008, pp. 1390–1397.
F2944 − 20
RELATED MATERIAL
E29 Practice for Using Significant Digits in Test Data to Determine Conformance with Specifications
E177 Practice for Use of the Terms Precision and Bias in ASTM Test Methods
E456 Terminology Relating to Quality and Statistics
E691 Practice for Conducting an Interlaboratory Study to Determine the Precision of a Test Method
F1469 Guide for Conducting a Repeatability and Reproducibility Study on Test Equipment for Nondestructive Testing
F2149 Test Method for Automated Analyses of Cells—The Electrical Sensing Zone Method of Enumerating and Sizing Single Cell Suspensions
ASTM International takes no position respecting the validity of any patent rights asserted in connection with any item mentioned
in this standard. Users of this standard are expressly advised that determination of the validity of any such patent rights, and the risk
of infringement of such rights, are entirely their own responsibility.
This standard is subject to revision at any time by the responsible technical committee and must be reviewed every five years and
if not revised, either reapproved or withdrawn. Your comments are invited either for revision of this standard or for additional standards
and should be addressed to ASTM International Headquarters. Your comments will receive careful consideration at a meeting of the
responsible technical committee, which you may attend. If you feel that your comments have not received a fair hearing you should
make your views known to the ASTM Committee on Standards, at the address shown below.
This standard is copyrighted by ASTM International, 100 Barr Harbor Drive, PO Box C700, West Conshohocken, PA 19428-2959,
United States. Individual reprints (single or multiple copies) of this standard may be obtained by contacting ASTM at the above
address or at 610-832-9585 (phone), 610-832-9555 (fax), or [e-mail] (e-mail); or through the ASTM website
(www.astm.org). Permission rights to photocopy the standard may also be secured from the Copyright Clearance Center, 222
Rosewood Drive, Danvers, MA 01923, Tel: [phone]; http://www.copyright.com/
1. Scope
1.1 This practice, provided its limitations are u
...