Fit-for-purpose considerations for cell 

counting using image cytometry

April 10, 2017

Cell Counting Workshop

Jean Qiu, PhD

CTO, Founder

Nexcelom Bioscience
Does cell counting produce better results?

0.0

2.0

4.0

6.0

8.0

10.0

12.0

0 50 100 150 200

ECAR (mpH/min/ug)

Time (min)

DCIS GST HCT116 GSt

0.0

1.0

2.0

3.0

4.0

5.0

6.0

7.0

8.0

9.0

10.0

0 50 100 150 200

ECAR (mpH/min/*10-4 Cells)

Time (min)

DCIS GST HCT116 GSt

Glycolytic function measured using Seahorse 

Normalization using protein 

quantity Normalization using cell count
Which cell counter is right for me?

It depends!

Generalized framework for designing and conducting 

cell measurements

• Well defined quality attribute

• Well-designed measurement that are fit-for-purpose

• Robust measurement with built-in measurement assurance

• Appropriate documentation, reporting, and communication

Lin-Gibson et al., Cytotherapy. 2016 Oct; 18 (10):1241-4

Fit-for-purpose investigation is 

the most important work prior 

to verification and validation.
Three key fit-for-purpose considerations for cell 

counting 

1. Understand cell preparation 

– What types of cells are in the cell preparation?

2. Evaluate cell counting assays for selectivity for the desired cell type

– How do each cell counting assay work? Does it have the selectivity to count desired cell 

type within this cell preparation?

3. Determine tolerance in sample processing prior to cell counting 

– How much processing steps are acceptable before cell counting?
Three use cases to illustrate these key 

considerations for fit-for-purpose

• Case I: count cells for large tumor cell line panels used for oncology 

drug screening

• Case II: count cells for HPC based cellular processing

• Case III: count cells for mouse experiments
A few definitions

• Biological operation

– A series of experiments to produce results

• Cell preparation

– Cell sample to be analyzed for cell count

• Cell counting assay

– The specific measurement to obtain cell counts

• Cell counting system

– Hardware, software, reagents to produce cell counting results

A laboratory conducts multiple 

biological operations with varied cell 

counting needs 
Understand cell preparation for image-based 

cell counting 

Potential impurities

Suspension medium

Cells Cell type2

Cell type1

Cell type3

Particle debris

Chemical impurities

Focus of the 

image based 

cell counting 

assays
Image-based cell counting principles

1. Pipette 20uL of cells into 

disposable counting 

chamber 

2. Insert chamber in 

Cellometer and click 

count

3. Cell count data is 

generated automatically
Cell counting assay using bright-field image

Most of the time you see 

this on a product literature

Real life can be messy
FL staining can be more specific for some cell 

types - cadiomyocytes

AO stains all cells

PI stains dead cells

BF is hard to quantify 
Cell counting assay using dual fluorescence 

nuclear stains

• AO is permeable to both live and dead cells

• AO binds to DNA and fluoresce bright green

• PI can only enter dead cells 

– Binds to DNA of the dead cells

– Absorbs the green fluorescence of AO 

– Produces bright orange / red color

• No signal is generated from non- nucleated cells and debris

• Original AO/EB staining protocol developed at Stanford, 

Prof. Leonard Herzenberg’s lab

• Nature Protocols, Vol 8.No. 1, 33, 2013

Identification, isolation and in vitro expansion of human and nonhuman 

primate T stem cell memory cells

Enrico Lugli, Luca Gattinoni, Alessandra Roberto, Domenica Mavilio, David A Price, 

Nicholas P Restifo & Mario Roederer
Cell counting fit-for-purpose case I: 

oncology screening projects using 

human cell line panels
NCI 60 cancer cell line panel

13

• NCI-60 cancer cell line panel defined by National Cancer Institute

• Used for screening of promising cancer therapeutics

• Over 3,000 distinct compounds screened each year

Source Cell Lines

Lung NCI-H23, NCI-H522, A549-ATCC, EKVX, NCI-H226, NCI-H332M, H460, H0P62, HOP92

Colon HT29, HCC-2998, HCT116, SW620, COLO205, HCT15, KM12

Breast MCF7, MCF7ADRr, MDAMB231, HS578T, MDAMB435, MDN, BT549, T47D

Ovarian OVCAR3, OVCAR4, OVCAR5, OVCAR8, IGROV1, SKOV3

Leukemia CCRFCEM, K562, MOLT4, HL60, RPMI8266, SR

Renal UO31, SN12C, A498, CAKI1, RXF393, 7860, ACHN, TK10

Melanoma LOXIMVI, MALME3M, SKMEL2, SKMEL5, SKMEL28, M14, UACC62, UACC257

Prostate PC3, DU145

CNS SNB19, SNB75, U251, SF268, SF295, SM539
Much larger cell line panels

• Novartis /Broad (CCEL) - 1,000 cell types

• MGH/Sanger – more than 1,600 cell types
Purpose of the cell counting

• Count cells for cell line expansion

• Count cells to seed assay plates (1536w, 384w, 96w)
What do cell preparations look like for NCI-60 

cell lines? 

With very large variations
Summary of NCI 60 diverse morphologies

Cell Characteristics Description (trypsinized) % Total

Round with narrow size 

distribution

Cells with round shape, not sticking to each other

Not a very large size distribution

43%

Clumpy Cells stick to each other forming clumps,

typically more than 5 cells per clump 

57%

Clustering Cells stick to each other forming clusters,

typically less than 5 cells per cluster

Debris Dead cells form debris in suspension

Large Morphology Variation Cells vary in size and shape
Cell Membrane Outline Algorithm to address the 

morphological diversity for counting

Advanced imaging and analysis algorithms are necessary to address 

the cell morphology diversity in the large tumor cell lines.

Algorithms Identify irregular shapes

(SK28 cell line)

Algorithms identify 

cell clusters
Case I summary

• Each cell preparation contains only one cell type

• Cell preparation is generally in heathy conditions with viability higher than 

90%

• Bright-field based cell counting assay can be used

• Special image analysis capability is required for devise morphologies
Cell counting fit-for-purpose case II: 

cell therapy bio-processing
Examples of cell therapy production processes

• CAR-T manufacturing

• Hematopoietic stem cell 

• Therapeutic cancer vaccine
Purpose of the cell counting

• Count cells from starting material, in-processing material and the final 

products
Source of the starting material

PBMC by Ficoll separation Leukopak from leukophoresis

Other source materials: bone marrow, cord blood
What cell types are in the starting material? 
Is the bright-field based counting method 

selective enough for staring materials?

Total cells: 119

Total FL cells: 39

Over counted cells: 80

They are not nucleated.

Over counting: 3x

Starting material from Leukopheresis
Multiple Leuokopheresis samples shown varied 

amount of RBC

• Manual counting using Hemacytometer and trypan blue

• Automatic cell counting using Cellometer Vision and AO/PI stains

• Experiment using Leukopheresis samples

Sample ID A B C D E F

Ratio

(Manual counting to 

Cellometer AO/PI) 7 5 12 3 7 5
TNC using manual and automatic cell counting with 

good correlation 

y = 0.9113x

R2 = 0.9891

1.0E+06

1.0E+07

1.0E+08

1.0E+06 1.0E+07 1.0E+08

TNC (AO/PI by Auto 2000)

Manual TNC (cells/ml)

• Manual counting using Hemacytometer post lysing red blood cells

• Automatic cell counting directly stain starting material with AO/PI prior to cell 

counting

• Starting materials were bone marrow, leukopak, cord blood
Select a cell counting system with multiple cell 

counting assays

Source materials:

PB, BM, CB

In processing: 

MNC

Finished product: 

CD34+ cells

Bright-field 

image

Fluorescent

image

AO/PI Trypan blue
More finished products with cell preparation containing 

only one cell type, no particle debris

Bright-field cell counting assay using trypan blue works well with finished products. 
Case II summary

• Cell preparation changes though cell process

• Without lysing, fluorescence staining method is more selective

• Bright-field based cell counting assay can be used for finished product 

when cell preparation is 

• Cell counting system provides multiple assays are essential to satisfy 

varied cell preparation conditions
Cell counting fit-for-purpose case III: 

primary cells from mouse models
Example cell types and assays from mouse models

• Tissues

– Blood, Spleen, BAL, Lung

– Axillary LN, Mesenteric LN, Inguinal LN, Obturator LN, Iliac (Internal) LN, 

Cervical LN, Mesenteric LN , Bronchial LN

– Primary bronchi, Tonsils, Nares Mucosa, Trachea, Rectum, Jejunum, Cervix, 

Vagina, Uterus, Ileac

• Cells are used for:

– FACs Surface and Intracellular Staining

– Elispots

– qPCR
Cell preparation evaluation

• The operation contains multiple cell preparations

• All cell preparation contains multiple cells types

• Most cell preparation contains particular debris from tissue digestion 

process

• The amount of cells vary from one animal to another animal
What is in a spleenocyte cell preparation?

BR AOPI
What is in mouse tail blood?
What is in BAL?

BR AOPI
Requirements from mouse samples: large dynamic 

range in both cell count and viability

Results from analysis of 30 distinct lymphocyte samples

Sample # 7 Sample # 6 Sample # 2 Sample # 5

Total cell count 49 1419 10635 24245

Live cell 

concentration

1.07E+05 2.84E+06 3.11E+07 6.75E+07

Viability 63.2% 58.0% 84.4% 80.3%

Bright field cell 

image

Counted live 

/dead cell image
Case III summary

• No tolerance in lysing and other cell processing steps prior to counting –

large number of samples per run

• Large variation in the amount and type of particle debris due to tissue 

source and dissociation processes

• Cell concentration and viability vary in several order of magnitude

• Bright-field based cell counting assay cannot be used

• Dual FL stain provided good cell counting assay
Q&A

Jean Qiu

jqiu@nexcelom.com

Three key fit-for-purpose considerations for 

cell counting using imaging cytometry

1. Understand cell preparation 

2. Evaluate cell counting assays for selectivity 

3. Determine tolerance in sample processing