& Gene Therapy · recent book, ‘The Emperor of All Maladies: A Biography of Cancer’ [4],...

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CELL & GENE THERAPY INSIGHTS

CELL THERAPY CMC/QUALITY & ANALYTICS

EXPERT INSIGHT

Standards Landscape in Cell Counting: Implications for Cell & Gene TherapySumona Sarkar, Laura Pierce, Sheng Lin-Gibson & Steven P Lund

Recent advances in the fields of cell and gene therapy and regenera-tive medicine have increased the need for a standardized approach to cell counting. International efforts in the development of documentary standards as well as workshops which include input from device manu-facturers, regulators, researchers and manufacturing organizations have sought to increase confidence in cell count measurements. Improving comparability between methods, enabling fit-for-purpose method selec-tion, and facilitating the translation of cell counting measurements be-tween stages of product development are critical for the success of cell and gene therapy products. Here, we describe recent efforts in the area of standards development for cell counting and outline a technical ap-proach for the comparison and selection of cell counting methods in the absence of reference materials. These standards and approaches offer strategies for the development and implementation of counting methods that are fit-for-purpose to address the broad needs of cell and gene ther-apy and regenerative medicine.

Submitted for Peer Review: 11 Dec 2018 u Published: 14 Feb 2019

Cell & Gene Therapy Insights 2019; 5(1), 117–131

DOI: 10.18609/cgti.2019.016


DOI: 10.18609/cgti.2019.016118

INTRODUCTION

For cell and gene therapy prod-ucts, where cells are a part of the final product, cell characterization is critically important. Robust, accurate and precise methods are needed to enable manufacturing control and product release [1]. Of these cell characterization methods, cell counting methods (including measurements such as cell viability and cell proliferation] are among the most routinely used [2,3]. Cell counting has been conducted in the biological sciences for over a centu-ry with important medical discov-eries having hinged on the ability to count cells. For example, in his recent book, ‘The Emperor of All Maladies: A Biography of Cancer’ [4], Siddartha Mukherjee empha-sizes the critical role of blood cell counting in the development of cancer therapies and the progres-sion of cancer research:

“…leukemia was different from nearly every other

type of cancer … Leukemia, floating freely in the blood,

could be measured as easily as blood cells. If

leukemia could be counted, Farber reasoned, then any intervention – a chemical sent circulating through

the blood, say – could be evaluated for its potency

in living patients. He could watch cells grow or die in the blood and use that to measure the success or

failure of a drug. He could perform ‘experiments’ on

cancer.”In the 21st century, cell count based measurements continue to

underpin key decision making throughout the biotechnology product development pipeline. Cell count is used to evaluate cell growth, health and function (in-cluding cell count for other critical assays such as proliferation, meta-bolic and enzymatic activity assays) and more recently to establish the dose of cellular therapeutic prod-ucts [3]. While historically, cell counting was conducted primarily through microscopic visualization of cells and manual identification and enumeration, several other modalities have been developed to count cells (e.g., electrical sensing, flow cytometry, automated imag-ing). Given the many approach-es to cell counting, as well as the diverse application of cell count-based measurements, there is a need to establish approaches that support cell counting analytical development.

A recent NIST-FDA Work-shop on ‘Sharing practices in cell counting measurements’ brought together cell counting instrument manufacturers, cell therapy devel-opers, regulators, and government laboratories to evaluate the state of cell counting and identify best practices [3]. The workshop report highlights the need for: (i) docu-mentary standards and best-prac-tice guides; (ii) consistent ter-minology; (iii) development of biological and non-biological ref-erence materials; and (iv) tailored measurement strategies based on fit-for-purpose needs [3]. Here we further discuss existing cell count-ing-based standards and describe recent efforts in the development of general standards and ap-proaches applicable to improving cell counting confidence in cell and gene therapy.

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119Cell & Gene Therapy Insights - ISSN: 2059-7800

BRIEF REVIEW OF EXISTING STANDARDS IN CELL COUNTING

Due to the fundamental nature of the cell counting measurement, several cell counting standards have been developed to facilitate implementation of counting based methods for specific applications and improve comparability be-tween results (Table 1). The majority of existing standards are developed for a particular biotechnology sec-tor and are either test article spe-cific, test method specific, or both. The standards describe aspects of sample handling, instrumentation, data analysis and method valida-tion, including approaches to eval-uate method accuracy, precision, robustness and linearity. In most instances, cell counts are repeat-edly conducted on similar types of cell samples for routine analysis (e.g., blood products, milk prod-ucts, etc.) making these counting methods amenable to establish-ing standardized methods for cell counting. The field of cell and gene therapy presents a new challenge to establishing standardized cell counting methods. Cell and gene therapies represent a wide range of biological samples that need to be evaluated for cell count. Different cell types, different formulations, wide biological variability, and numerous cell counting modali-ties make it difficult to envision a single reference method or refer-ence material that can satisfy all needs. As a result, in addition to the development of specific stan-dards, efforts have recently focused on establishing more general cell counting standards and tools that can address the broad needs of cell therapy and regenerative medicine.

ISO STANDARD FOR GENERAL GUIDANCE ON CELL COUNTING METHODS

The International Organization of Standards (ISO) Technical Com-mittee (TC) 276 for Biotechnol-ogy (ISO/TC 276) has recently published a standard entitled ISO 20391-1:2018 Biotechnology – Cell counting – Part 1: General guidance on cell counting meth-ods [17]. This two-part document focuses on general approaches to improve confidence in cell counting over a wide range of applications, sectors, and test articles. In the first part of the standard, the focus is on providing general guidance on cell counting methods including aspects of terminology, method selection, measurement process control, and data analysis and reporting (Table 2).

Cell counting measurements are broken down into four main categories: total cell count, dif-ferential cell count (e.g., viable or non-viable subsets of the total population), direct cell count (e.g., nuclear staining and enumeration of individual cells), and indirect cell count (e.g., population-based evaluation of metabolic or enzy-matic activity) with potential over-lap between categories. Through this breakdown, users can identify considerations specific to the cell counting category in which their measurement falls. For example, a direct cell counting method, which involves the recording of a signal or a set of signals from each cell, gen-erally requires cells to be well dis-persed and easily discernable from debris and other particulates in a cell sample. Indirect cell counting methods that involve the record-ing of a signal from all cells (or a

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120120 DOI: 10.18609/cgti.2016.014 Cell & Gene Therapy Insights - ISSN: 2059-7800

f TABLE 1Examples of cell count related standards.

SDO Standardnumber

Standard title Applicability Publication status

ASTMi E2526–08 Standard Test Method for Evaluation of Cytotoxicity of Nanoparticulate Materials in Porcine Kidney Cells and Human Hepatocarcinoma Cells [5]

Sector specific Published 2008; reapproved 2013

ASTMi F 2149-01 Standard Test Method for Automated Analyses of Cells – the Electrical Sensing Zone Method of Enumerating and Sizing Single Cell Suspensions [6]

Test method specific Published 2001; reapproved 2016

ASTMi F2944-12 Standard Test Method for Automated Colony Forming Unit (CFU) Assays — ImageAcquisition and Analysis Method for Enumerating and Characterizing Cells and Colonies in Culture [7]

Test method specific Published 2012

ASTMi F2739-16 Standard Guide for Quantifying Cell Viability within Biomaterial Scaffolds [8] Test article specific Published 2008; revised in 2016

ASTMi WK62115 New Test Method for Measuring Cell Viability in a Scaffold Test article specific Draft Under Development; initiated 2018

CLSI H07-A3 Procedure for Determining Packed Cell Volume by the Microhematocrit Method, 3rd Edition [9] Test method specific Published 2000

CLSI H20-A2 Reference Leukocyte (WBC) Differential Count (Proportional) and Evaluation ofInstrumental Methods; Approved Standard – Second Edition [10]

Test article specific Published 2007

CLSI H26-A2 Validation, Verification, and Quality Assurance of Automated Hematology Analyzers, 2nd Edition [11] Test method specific Published 2010; reviewed and confirmed 2016

CLSI H42-A2 Enumeration of Immunologically Defined Cell Populations by Flow Cytometry, 2nd Edition [12] Sector Specific Published 2007; reviewed and confirmed 2017

DIN 58932-1 Haematology – Determination of the concentration of blood corpuscles in blood – Parts 1–5 [13] Test article specific Part 1 Published 1997; Current Edition 2012

ISO 19007:2018 Nanotechnologies – In vitro MTS assay for measuring the cytotoxic effect of nanoparticles [14] Sector specific Published 2018

ISO 13366-1:2008 Milk – Enumeration of somatic cells – Part 1: Microscopic method (Reference method) [15] Sector specific Published 2008; reviewed and confirmed in 2011

ISO 13366-2:2006 Milk – Enumeration of somatic cells – Part 2: Guidance on the operation of fluoro-opto-electronic counters [16] Sector specific Published 2006

ISO 20391-1:2018 Biotechnology – Cell counting – Part 1: General guidance on cell counting methods [17] General Published 2018

ISO DIS 20391-2 Biotechnology – Cell Counting – Part 2: Experimental design and statistical analysis to quantify counting method performance

General Initiated 2015; expected Ppublication 2019 (pending approval)

USP 127 Flow Cytometric Enumeration of CD34+ Cells with USP CD34+ Cell Enumeration System Suitability Reference Standard [18]

Test method specific Published 2017

ASTMi: American Society for Testing and Materials International; CLSI: Clinical and Laboratory Standards Institute; DIN: German Institute of Standardization; ISO: International Organization for Standardization; SDO: Standards Development Organization (note that not all organizations listed are voluntary consensus standards development organizations).

EXPERT INSIGHT


subset of cells) in the sample and then relating that signal to a cell count based on calibration curve, are instead more reliant on hav-ing appropriate reference materials and accurate calibration curves. The document further provides points to consider in developing control strategies for cell count-ing methods, including consider-ations for the use of appropriate reference materials. Importantly, ISO 20391-1:2018 requires that cell counting measurement meth-ods be validated, where method performance parameters are estab-lished to provide evidence that the method produces results suitable for the intended purpose [17]. The document further requires users to develop a data report that contains

sufficient detail to allow indepen-dent assessment of the cell count results, with suggestions for the content of the report [17]. These requirements will assure that all us-ers of the standard have addressed a common level of considerations for their cell counting method, and a common understanding of the doc-umentation and reporting required to facilitate the use of count-based data in critical decision-making.

Following ISO principles, ISO 20391-1:2018 was developed through an international consen-sus process with input from regula-tors, device manufacturers, device users, national metrology insti-tutes and academic/translational centers [3]. Thus, the document is considered a voluntary consensus

f TABLE 2Concepts described in ISO 20391-1:2018 Biotechnology – Cell counting – Part 1: General guidance on cell counting methods [17].

General concepts of cell counting fTotal cell counting

fDifferential cell counting

fDirect cell counting

fIndirect cell countingConsiderations for cell counting measurements

fSelection of a cell counting method

fConsiderations for selecting a cell counting method

fSampling of cells for counting

fPreparation of cell samples for counting (e.g. environmental factors, handling procedures, quality/stability of reagents)

Qualification, validation and verification fInstrument qualification

fMethod validation and verification

fReference materials (e.g., certified reference materials, in-house reference materials)

Data processing, analysis and reporting fData processing and analysis (e.g., image processing, gating strategies, coincidence correction)

fReportingDescription of common cell counting meth-ods and their categorization

fE.g., Impedance-based counting, flow cytometry, automated image analysis, metabolic assays


DOI: 10.18609/cgti.2019.016122

standard (governments may choose to reference a voluntary consensus standard in a regulation) [19].

Other peer-reviewed publica-tions and instrument manufacturer application notes are available that also outline common best-practic-es, describe new or improved meth-ods for cell counting, and compare various cell counting methods for specific applications [3,20–34]. These documents are very helpful in providing more detailed infor-mation regarding cell counting and providing important examples of cell counting method develop-ment and evaluation; however, they are limited in their impact on the global community as there is a narrow basis for widespread adop-tion and conclusions are often sys-tem specific. Voluntary consensus standards on the other hand, due

to their rigorous development pro-cess with a broad range of input, provide a means to place all inter-ested parties on a common ground for communication and integra-tion (Figure 1) [19,35–37].

BENEFITS FOR THE IMPLEMENTATION OF CELL COUNTING STANDARDSThe development and implementa-tion of cell counting standards for cell and gene therapy offer several tools and techniques to the biotech-nology community. Cell counting device providers will have a com-mon language with which to inter-face and discuss cell counting needs and capabilities with cell counting users. Cell therapy developers can be confident that they are address-ing critical aspects of cell counting early in their development process, easing transfer of methods to qual-ity control and manufacturing. Also, of critical importance is the role of standards in facilitating translation between different units that conduct cell counts (e.g., aca-demic centers translating to larger cell therapy developers or contract manufacturers, or analytical de-velopment translating counting methods to quality control). If cell counting standards are adopted early in the development pipeline, and critical aspects of cell count-ing are adequately controlled for, transfer of methods between sites or between operating units will be greatly facilitated. Finally, the ap-plication of cell counting standards should help to elevate the quality of this measurement quickly and broadly across the field, such that companies will need to spend less

f FIGURE 1Schematic illustrating the role of cell counting standards in pro-viding a common ground among many aspects of the cell therapy development ecosystem.

EXPERT INSIGHT


resources on the development of this pre-competitive fundamental measurement.

As illustrated in Figure 1, cell counting standards are poised to play an increasingly important role in the development of cell-based therapy products and will serve as references throughout many or-ganizations in the biotechnology landscape.

A STANDARD APPROACH TO EVALUATE QUALITY OF CELL COUNTING METHODSCell counting represents a wide range of counting methods and in-struments, based on several differ-ent biological principles to identify and enumerate cells. The diversity of cell counting approaches is often necessary given the breadth of sam-ples that need to be counted as well as more practical considerations such as cost, speed of measurement, and ease of use [3]. In autologous cell therapy product manufacturing for example, counts are needed at all stages of the process from apher-esis and cell expansion to product release, with each stage possibly re-quiring a different counting modal-ity or instrument settings to meet specific analytical requirements.

Given the unique qualities of cell and gene therapy products, it is especially important to develop fit-for-purpose counting methods that can vary from product-to-product, and stage-to-stage in product devel-opment. Standardized approaches that facilitate the selection, devel-opment, and validation of fit-for-purpose cell counting methods will greatly improve the process of cell counting method development.

As listed in Table 1, several sector/application-specific cell counting standards exist. Many of these stan-dards use a comparability approach in which results from a newer meth-od are benchmarked to the results obtained from a more established method. This approach however does not address the quality of ei-ther measurement process. There remains a need to develop strategies to provide assurance for the quali-ty of a cell counting measurement process in the absence of a reference material or reference method.

Recently NIST led the develop-ment of an approach to evaluate fundamental aspects of the quality of cell counting methods [31]. This approach utilizes a dilution series experimental design to evaluate pre-cision and proportionality of cell counting measurements. Precision refers to repeatability of measure-ments while proportionality refers to the closeness of agreement to a proportional model fit when evalu-ating dilution series data. The prop-erties of precision and proportion-ality are fundamental to confidently utilizing cell count-based data. The evaluation of proportionality to di-lution is specifically useful when a reference material is not available. In this case, a well-controlled dilu-tion series serves as an internal con-trol to evaluate a relative accuracy: is the measurement self-consistent [31]? If a cell count is to be self-con-sistent across a certain range of cell concentrations, the measurement will necessarily be proportional to the dilution fraction used to pro-duce samples (from a common stock solution) within that range as well. Any deviation from pro-portionality will indicate an error in the measurement (systematic or non-systematic).


DOI: 10.18609/cgti.2019.016124

The experimental design de-scribed by Sarkar et al. [31] con-sists of a dilution fraction series in which replicate samples at each dilution are individually prepared from a stock solution through in-dependent and verified dilutions. Replicate observations are also ob-tained for evaluation of precision (Table 3). Metadata such as oper-ator, time elapsed, and other ob-servations that may be relevant to the sample measurements are also recorded.

Precision (% coefficient of vari-ation [%CV]) is calculated based on replicate observations, and a proportionality index (PI) is eval-uated based on smoothed residuals to assess systematic deviation from proportionality. R2, or coefficient of determination, a measurement of how closely the data points fall to a fitted regression line, is also evaluated as a more traditional ap-proach to evaluate the goodness of the proportional model fit, howev-er this approach accounts for both

systematic deviation from propor-tionality as well as contributions from imprecision. Further descrip-tion of the general experimental design, dilution verification, and statistical analysis is described in Sarkar et al. [31].

CASE STUDY COMPARING THE QUALITY OF CELL COUNTING METHODS USING THE DILUTION SERIES EXPERIMENTAL DESIGN & STATISTICAL ANALYSIS FRAMEWORKCertain commercial equipment, in-struments or materials are identified in this paper to specify the experi-mental procedure adequately. Such identification is not intended to imply recommendation or endorse-ment by the National Institute of Standards and Technology, nor is it intended to imply that the materials or equipment identified are necessar-ily the best available for the purpose.

f TABLE 3Experimental design considerations for using a dilution series study to evaluate as-pects of the quality of cell counting methods [31].

Experimental de-sign consideration

Approach Purpose

Stock solution Homogeneous stock solution Supports generating representative test samples

Common stock solution between methods

Allows direct comparison between different measurement methods

Dilution method Independent dilution fractions Reduces confounding and error propagation between samples

Replication Replicate samples per dilution fraction

Allows assessment of variability between samples

Replicate observations per sample

Allows assessment of measurement preci-sion (CV)

Dilution verification

Verified/Qualified pipetting Reduces error introduced by pipetting and allows use of dilution fraction as an internal reference to assess proportionality

Operator bias Blinded random labeling and ran-domized measurement order

Reduces operator bias and temporal bias

EXPERT INSIGHT


The following case study illus-trates an application of the dilu-tion series experimental design and statistical analysis in comparing the quality of three cell counting methods, to help facilitate method selection. In this case study, a cell counting method is being devel-oped to evaluate the total cell con-centration of Jurkat cells. Method 1 utilizes aautomated trypan blue dye exclusion method, Method 2 uses an automated acridine orange fluorescent stain with a propidium iodide (PI) counterstain dye exclu-sion method, and Method 3 uses

an automated acridine orange with a 4′,6-diamidino-2-phenylindole (DAPI) counterstain dye exclu-sion method. Figure 2 illustrates the experimental design used in this study, evaluating five Jurkat cell (ATCC) concentrations across a range of approximately 0.2 x 106 cells/ml to 2.0 x 106 cells/ml. Jur-kat cells were nutrient-deprived (cultured without the addition of fresh media) for 10 days to obtain a stock cell sample of approximate-ly 70% viability (as reported from counting Method 1). Utilizing cell samples with a combination of

f FIGURE 2The case study experimental design consisted of a stock cell solution that was diluted independently into five dilution fractions (cell culture media was used as diluent).

Four replicate sample tubes were prepared from each dilution fraction, and random numbers assigned as labels. Each sample tube was then split into three individual tubes (one tube per method tested). From each of those tubes, three replicate observations were measured according to the method specified.


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live, dead and un-healthy cells is important when evaluating count-ing methods that are either intend-ed to enumerate all cell conditions or discern live cells from dead cells. In addition to the approach we used here to induce cell death under physiologically relevant conditions, others have also generated prepared mixtures of live and dead cells (e.g., fixed cells or heat shock killed cells) at pre-determined ratios to assess cell counting methods and evalu-ate linearity of percent recovery of live/dead cells in counting. In the absence of dead and/or dying cells, a method’s ability to correctly iden-tify and enumerate the different cell populations cannot be sufficiently characterized.

In evaluating the mean cell con-centration across methods (Fig-ure 3), it is evident that not all of the methods result in similar cell concentrations across the dilutions investigated. Systematic bias, or systematic differences in cell count, can be quantified by comparing the slopes of the proportional model fit (i.e., the proportionality constant). Based on this analysis, the propor-tionality constant associated with

Method 1 differs significantly from those of Methods 2 and 3, while Methods 2 and 3 do not indicate a statistically significant difference from each other in terms of system-atic bias (Table 4). The analysis of systematic bias however does not in-dicate if any of these three methods have inherent problems with qual-ity of the measurement. To further evaluate the quality of the methods we calculate each method’s preci-sion and proportionality (Figure 4). Although Method 2 and Method 3 do not demonstrate significant systematic bias from one another, %CV, PI and R2 indicate that sig-nificant differences in precision and proportionality exist between the two methods (Table 4). Specifically, Method 3 appears to have a higher level of precision than Method 2, but deviation from proportionality (higher PI value) indicates a less pro-portional response in comparison to Method 2. Method 1 was observed to have greater proportionality than Method 3 as well as higher precision (lower %CV) than both methods at four of the five dilution fractions tested. In this case, the higher de-viation from proportionality of

f FIGURE 3Dilution series data based on the experimental design outlined in Figure 2 for three cell counting methods.

Each data point represents the average total cell concentration (cells/mL) across three replicate observations. The line shown for each figure represents a proportional model fit using a weighted least squares modelling approach with a quasi-Poisson assumption for the mean-variance relationship (i.e., variance proportional to the mean).

EXPERT INSIGHT


Method 3 may need to be investi-gated and improved in order to se-lect this method for evaluating the total concentration of the Jurkat cells. Method 1 and Method 2 both demonstrate a high degree of pro-portionality and precision, making

both methods possible candidates for evaluating total cell concentra-tion for the cells samples investi-gated. Interestingly, these methods have a significant bias relative to one another, and in the absence of a reference material or reference

f TABLE 4Comparison of methods with resulting significant differences in proportionality and bias observed.

Comparison Significant difference observed for R2

Significant difference observed for PIAbsSSR

Significant differ-ence observed for systematic bias

Method 1 vs method 2 Yes No YesMethod 1 vs method 3 Yes Yes YesMethod 2 vs method 3 Yes No No

Data was collected for a nutrient-starved Jurkat cell sample.

f FIGURE 4n = 4 for each method, for each data point shown.

1 2 3

Cell coun�ng method

0.8

0.9

1.0

R2

1 2 3

Cell coun�ng method

0

1

2

PIA

bsSS

R

3

(D) Propor�onality index (PI)(C) R2 for propor�onal model fit

Target DF (dfi)Target DF (dfi)0.1

00.3 0.5 0.7 0.9

10

20

30

(B) Coefficient of varia�on (%CV) ofrepeatability over replicate observa�ons

Mea

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CVdf

i (%)

(Err

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2x106

3x106

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(A) Mean cell concentra�on

(A) Mean cell concentration is plotted at each dilution fraction with error bars representing one standard deviation. (B) Precision is plotted as %CV for each dilution fraction. Error bars represent one standard deviation. (C) Resulting R2 value for each counting method is plotted with upper and lower 95% Confidence Intervals (CI) shown as error bars. (D) PIAbsSSR (smoothed scaled absolute value of residuals) is plotted with upper and lower 95% confidence intervals as error bars.


DOI: 10.18609/cgti.2019.016128

method, the absolute accuracy of ei-ther method cannot be established. Selecting between Method 1 and Method 2 would require evaluat-ing the fit-for-purpose needs of the methods, for example, correlation of results to other relevant biolog-ical phenomenon, considerations of intermediate precision, ease-of-use, robustness, etc. It is import-ant to note that this experimental design approach does not provide a true or absolute count and does not determine the accuracy of any given method. Errors in cell count that scale with proportionality may not be detected using this experi-mental design approach. However, as this example demonstrates, the dilution series experimental design and statistical analysis approach is a powerful tool in evaluating the precision and proportionality of cell counting methods and can be used to facilitate method selection in the absence of reference materials or for comparison to methods with un-confirmed accuracy.

BENEFITS OF A STANDARDIZED APPROACH TO EVALUATING THE QUALITY OF CELL COUNTING METHODSExperimental design and statisti-cal analysis approaches similar to the case study described here are currently being incorporated into an international standard in ISO TC 276 as a part of DIS 20391-2 (Biotechnology – Cell Count-ing – Part 2: Experimental design and statistical analysis to quantify counting method performance). If approved, this document would provide a general standardized

approach to quantify cell count-ing method performance across a broad range of cell counting meth-ods and cell counting applications. Cell therapy developers may refer-ence the standard approach in de-scribing the methodology used to select, develop, and evaluate their cell counting methods. Standard-ized reporting of the cell counting analytical development process would further support rapid review of these procedures [35,36]. By utilizing a standard approach for method evaluation, faster and easi-er translation of analytical methods may occur between cell therapy developers and contract manufac-turing organizations by supporting the justification for cell counting method selection. At the analyti-cal development stage, a standard-ized approach will allow for more thoughtful selection and validation of cell counting methods, instru-mentation, and protocols, as the design can be used to specifically and directly compare statistical attributes of different counting methods. This design will allow de-velopers and manufacturers to gain improved harmonization of their methods and facilitate interopera-bility of counting methods across the research, development, and production phases of industry. De-vice manufacturers can also use a standard approach to evaluate their instrumentation and device users will have a standardized approach to compare counting methods based on critical fit-for-purpose considerations.

CONCLUSIONSRecent advances in the fields of cell and gene therapy have increased

EXPERT INSIGHT


the need for a standardized ap-proach to cell counting. Standards can improve comparability between methods and facilities, enable more fit-for-purpose method selection, and facilitate the translation of cell therapy research products. Here, we have described recent efforts in standards development for cell counting and described a technical approach for the comparison and selection of cell counting methods using experimental design and sta-tistical metrics. These standard ap-proaches, which are currently under development, offer strategies for the selection of more reliable and

robust counting methods for the fit-for-purpose needs of the biotech-nology industry, and will ultimately improve confidence in cell counting based measurements across the cell and gene therapy community.

FINANCIAL & COMPETING INTERESTS DISCLOSURE

The authors have no commercial, proprietary, or financial interest in the products or methods described in this article. Official contribution of the National Institute of Standards and Technology; not subject to copyright in the USA.

This work is licensed under

a Creative Commons Attri-

bution – NonCommercial – NoDerivatives 4.0

International License

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AFFILIATIONS

Sumona Sarkar1* Author for Correspondence National Institute of Standards and Technology, Biosystems and Biomaterials Division, Gaithers-burg, MD, USA email: [email protected]

Laura Pierce

National Institute of Standards and Technology, Biosystems and Biomaterials Division, Gaithers-burg, MD, USA

Sheng Lin-Gibson

National Institute of Standards and Technology, Biosystems and Biomaterials Division, Gaithers-burg, MD, USA

P Lund

National Institute of Standards and Technology, Statistical Engi-neering Division, Gaithersburg, MD, USA

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