CellutionsVol 4: 2011The Newsletter for

Cell Biology Researchers

EMD Millipore is a division of Merck KGaA, Darmstadt, Germany

EVERY cell type. EVERY time.

Precise and Accurate Bead Counting Using the Scepter™ 2.0 Handheld Automated Cell Counter Page 8

Quantitative Measurement of Autophagy Using a Novel Flow Cytometry Assay Page 12

A Novel, Substrate-Agnostic Class III HDAC Activity Assay Page 18

Human PBMC Isolation and Counting Using the Scepter™ 2.0 Handheld Automated Cell Counter Page 21

LentiBrite™ Lentiviral Biosensors for Fluorescent Cellular Imaging: Analysis of Autophagosome Formation Page 25

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Precise and Accurate Counts and Viability Measurements Across Multiple Cell Lines Using the Muse™ Cell Count & Viability Assay page 3

2

PRODUCT HIGHLIGHT

Now, experience it. Smarter cell analysis with your own Muse™.

Revolutionize the way you analyze cell viability, apoptosis and cell cycle with the Muse™ Cell Analyzer. Using miniaturized fluorescence-based detection, a user-friendly interface and optimized assays, the Muse™ Cell Analyzer provides powerful cell analysis simply, accessibly, and affordably. Experience the new Muse™ Cell Analyzer and make smarter, faster and more accurate decisions about your experiments.

www.millipore.com/muse

Simply see more.Scan this 2D bar code with your mobile device.

Read the article on page 3 of this issue and learn how the Muse™ cell analyzer provides rapid and reliable determinations of viability and total cell count.

3

IntroductionThe assessment of cell concentration in combination with

viability is an important step in the characterization of

cell health. Cell concentration and viability information

can be used for monitoring proliferation rates, optimizing

growth conditions and normalizing cell data for further

studies, such as assessing the impacts of cytotoxic

compounds. Current methods that rely on multiple

instrument platforms to provide these answers reduce

flexibility, limit the ability to obtain comprehensive cell

health information and add increased costs to researchers.

Other, simpler methods provide inconsistent results due

to their dependence upon single-uptake dyes, which

do not effectively discriminate between the various

states of cellular demise. Therefore, there is a crucial

need for analytical methods that provide rapid, robust

and reproducible counts, viability and other cell health

indicators on a single device to enable the efficient, daily

execution of cellular research.

The Muse™ Cell Analyzer is a unique instrument that

enables multidimensional cell health analysis on a single

platform. The simplified format enables researchers of

varying backgrounds and experience levels to obtain a

comprehensive picture of cellular health. This small, robust

benchtop cell analyzer effortlessly guides users through

the acquisition and analysis of samples using mix-and-read

assays with a highly simplified and intuitive touchscreen

interface which delivers rapid measurements of cell

concentration, viability, apoptotic status, and cell cycle

distribution. Using multiparametric fluorescent detection

of individual cells via microcapillary flow technology, the

system enables highly sensitive and rapid detection of

cellular samples using minimal cell numbers.

The Muse™ Count & Viability Assay is a simple, rapid, assay

that provides cell concentration and viability information.

The assay utilizes a proprietary mix of two DNA intercalating

fluorescent dyes in a single reagent (Figure 1). One of the

dyes is membrane-permeant and will stain all cells with a

nucleus. The second dye only stains cells whose membranes

have been compromised and are dying or dead. This

combination allows for the discrimination of nucleated cells

from those without a nucleus or debris, and live cells from

dead or dying resulting in both accurate cell concentration

and viability results. Stained samples are then analyzed on

the Muse™ Cell Analyzer using a guided touchscreen user

interface. The Count & Viability Assay display results in an

easy-to-read results page with an optional plot display.

The use of dual fluorescent probes that clearly identify all

nucleated cells, live and dead, allows for greater sensitivity

and accuracy compared to colorimetric methods.

In this study, we show that the assay provides superior

performance to conventional viability and count

measurement by Trypan blue exclusion, and we validate the

assay using multiple suspension and adherent cell lines.

Precise and Accurate Counts and Viability Measurements Across Multiple Cell Lines Using the Muse™ Cell Count & Viability AssayKatherine Gillis, Julie Clor, Asima Khan, Kamala TyagarajanEMD Millipore Corporation

4

Materials and MethodsThe Muse™ Count & Viability Assay uses a highly simplified

workflow to provide count and viability results. Sample

preparation is very simple with the one-step addition of

the mix-and-read reagent.

Data from prepared samples were quickly acquired on the

Muse™ Cell Analyzer using the touchscreen and the Count

& Viability Software Module (Figure 2). Briefly, a user enters

the module and hits “Run Assay”. The touchscreen prompts

the user to load a sample and, through simple on-screen

instructions, guides the user through the optimization and

verification of settings.

Figure 2. The Muse™ Count& Viability Module requires just six steps to perform acquisition and analysis using a guided user interface. Concentration and viability results are displayed automatically at the completionof acquisition. Optional dotplots allow for visualization and further data manipulation.

Figure 1. Workflow (upper panel) and Principle (lower panel) for the Muse™ Count & Viability Assay. The assay utilizes a proprietary mix of two fluorescent DNA intercalating dyes to provide information on total cell concentration and viability (lower panel). One membrane permeable dye stains all cells with nuclei, allowing for the distinction of cellular debris from cells without a nucleus. The second dye only stains cells whose membranes have been compromised. Dying and dead cells stain with both dyes, but dying cells have lower fluorescence intensity than do dead cells.

Add Muse™ Count & Viability

Reagent

Add cells to tube

Incubate for 5 minutes at

room temperature

Viable cells

Dead cellsDebris

Viable cells

Dead cellsDebris

Debris and non-nucleated cells

Viable Cells

Dying Cells

Dead Cells

Cell Size IndexNucleated Cell Stain

Viability Stain

LowNegNeg

HighHighNeg

Med to HighHighMed

HighHighHigh

Anticipated Staining Pattern and Cell Size Index

The user then enters sample-specific information and

then touches “Run Sample.” The instrument displays the

results screen with the calculated concentration values,

viability data, and user-input dilution factor and sample

characteristics. The instrument provides the user the option

to view the dotplot as well as adjust markers between

samples (Figure 2, bottom).

Data can be stored on the device, exported in a report

format and/or exported as a Microsoft Excel® file, thus

enabling the production of a robust documentation trail

with experimental details preserved.

Select Module Load Sample Adjust Settings Acquire View Results

Read on Muse™ Cell Analyzer

5

ResultsCounting AccuracyThe counting accuracy and linearity of the Muse™ Cell

Analyzer were verified by measuring its ability to provide

counts on multiple dilutions of reference counting beads.

Figure 3 compares expected bead concentrations to bead

concentrations measured using the Muse™ Cell Analyzer

at multiple concentrations in the range of 1.0x104- 1X106

beads/mL. The slopes and correlation coefficients of linear

regression fit curves were both close to 1, demonstrating

that excellent counting accuracy and linearity could

be obtained using the Muse™ Cell Analyzer for the

concentration range tested for reference counting beads.

Versatility: Application to a variety of cell linesThe Muse™ Count & Viability Assay was used to determine

cell concentration across several cell lines, including

both suspension and adherent lines, at a variety of

concentrations. Figure 4 shows the comparison of

observed vs. expected cell concentrations for five of the

cell lines tested. The theoretical concentrations were

calculated based on the serial dilution of the original cell

sample, whose concentration was established using the

Muse™ Cell Analyzer. The slopes and R2 values for all the

cell lines tested closely approached 1, demonstrating that

Figure 3. Accurate counting of reference counting beads. A standard bead solution (whose concentration had been determined using a Coulter Counter®) was obtained from an external vendor and diluted over a concentration range of 1.0x104 to 1.0x106 particles/ mL. Expected bead concentrations were compared to observed bead concentrations. Each point represents the average of triplicate samplings and error bars represent corresponding standard deviations.

Table 1. Summary of cell lines tested to date. The cell lines tested represent commonly used lines in research laboratories. They include adherent cells, suspension cells, mammalian cell lines and an insect cell line.

Cell Line Name Adherent/Suspension Origin Source

Jurkat suspension Acute T Cell Leukemia — Human ATCC TIB-152

HL-60 suspension Promyelocytic Leukemia — Human ATCC CCL-240

HB-8307 suspension B Cell Myeloma — Human ATCC HB-8307

CHO adherent Ovarian — Chinese Hamster ATCC CCL-61

SF9 suspension Insect Ovary Spodoptera frugiperda Invitrogen 11496-015

K562 suspension Bone Marrow Chronic Myelogenous Leukemia — Human ATCC CCL-243

MCF-7 adherent Breast Adenocarcinoma — Human ATCC HTB-22

HeLa adherent Cervical Adenocarcinoma — Human ATCC CCL-2

PC-3 adherent Prostate Adenocarcinoma — Human ATCC CRL-1435

the assay provided linear responses across a wide range

of cell concentrations as well as diverse cell types.

Table 1 summarizes the list of cell lines validated to date

for use with the Muse™ Count & Viability Assay. The data

demonstrated accurate count and viability data for both

suspension and adherent cell lines over a range of sample

concentrations.

Figure 4. High linearity across multiple cell lines and a wide sample concentration range. The data show the comparison of observed vs. expected cell concentration results for serial dilutions of 5 representative cell lines shown, which include both adherent and suspension cells. Each point represents the average of three samplings.

HB (y = 0.9978x + 43390 R2 = 0.9996)

MCF-7 (y = 1.0081x + 143927 R2 = 0.9948)

K562 (y = 1.011x + 115434 R2 = 0.9949)

CHO (y = 0.9959x + 83983 R2 = 0.9991)

Jurkat (y = 1.0019x + 20520 R2 = 0.9997)

0.00E+00

2.00E+06

4.00E+06

6.00E+06

8.00E+06

1.00E+07

1.20E+07

1.40E+07

0.00E+00 2.00E+06 4.00E+06 6.00E+06 8.00E+06 1.00E+07 1.20E+07 1.40E+07

Expected Cell Concentration (cells/mL)

MCF-7 K562 CHO HB Jurkat

Obse

rved

Cel

l Con

cent

ratio

n (c

ells

/mL)

y = 1.0189x + 12898 R2 = 0.9989

0.00E+00

2.00E+05

4.00E+05

6.00E+05

8.00E+05

1.00E+06

1.20E+06

0.00E+00 2.00E+05 4.00E+05 6.00E+05 8.00E+05 1.00E+06 1.20E+06

Expected Bead Concentration (particles/mL)

Obse

rved

Bea

d Co

ncen

trat

ion

(par

ticle

s/m

L)

6

A B

Muse™ Cell Analyzer Manual Hemocytometer Automated Imaging-based Counting Device

Sample format required for acquisition Tube-based Slide-based Slide-based

Staining type Fluorescent dyes Trypan blue Trypan blue

Degree of operator bias Minimal Significant bias None

Variability in number of cells counted No variability Number of cells counted is concentration-dependent and may vary between samples

Number of cells counted is not clear, concentration-dependent

Number of cells counted More cells, increased statistical significance Fewer cells Fewer cells

Acquisition speed 1–2 minutes Slower due to manual counting ~ 1 minute

Flexibility in sample reading/analysis Greater flexibility in sample read time after staining

Samples must be analyzed soon after staining

Samples must be analyzed soon after staining

Data export features Advanced export features, reanalysis of data, allows for documentation of report; Excel® file export option

Lost after read; manually written-down results

Exportable to .csv file — only counts exported

Table 2. Comparison of features of Muse™ Cell Analyzer with features of other devices for measuring cell counts and viability.

Comparison of Muse™ counting compared to other counting systemsWe compared the accuracy of the Muse™ Count & Viability Assay with other methods that

provide count and viability information:

1. Traditional methods of cell counting that utilize Trypan blue staining such as manual

hemocytometer counts

2. Automated image-based analysis of Trypan blue-stained samples.

Table 2 summarizes the features of each of the three methods for cell concentration and

viability determination.

Five different cell lines at multiple concentrations and

viabilities were analyzed using the Muse™ Count & Viability

protocol and manufacturer-recommended protocols for

each of the other methods. Figure 5 depicts the comparison

of the average of triplicate measurements for each

individual cell counting method versus the average cell

concentration calculated by taking the mean average cell

concentration from all three methods together.

Regression statistics showed that the Muse™ cell analyzer

demonstrated excellent agreement with and provided

accurate results comparable to a variety of viability

methods and instruments.

0.00E+00

2.00E+06

4.00E+06

6.00E+06

8.00E+06

1.00E+07

1.20E+07

1.40E+07

0.00E+00 2.00E+06 4.00E+06 6.00E+06 8.00E+06 1.00E+07 1.20E+07 1.40E+07

Average Cell Concentration (cells/mL)

Cell

Conc

entr

atio

n (c

ells

/mL)

Image-based Automated Device

0.00E+00

2.00E+06

4.00E+06

6.00E+06

8.00E+06

1.00E+07

1.20E+07

1.40E+07

0.00E+00 2.00E+06 4.00E+06 6.00E+06 8.00E+06 1.00E+07 1.20E+07 1.40E+07

Average Cell Concentration (cells/mL)

Cell

Conc

entr

atio

n (c

ells

/mL)

Image-based Automated Devicey = 0.9574x - 42839 R2 = 0.9762

Manual Hemocytometery = 1.0696x - 138547 R2 = 0.9664

Muse™ Cell Analyzery = 0.973x + 181387 R2 = 0.9826

Manual Hemocytometer Muse™ Cell Analyzer

Image-based Automated Device Manual Hemocytometer Muse™ Cell Analyzer

Image-Based Automated Devicey = 0.9944x - 201508 R2 = 0.9697

Manual Hemocytometer y = 1.0779x - 19232 R2 = 0.9596

Muse™ Cell Analyzery = 0.9277x + 220740 R2 = 0.9942

0.00E+00

2.00E+06

4.00E+06

6.00E+06

8.00E+06

1.00E+07

1.20E+07

1.40E+07

0.00E+00 2.00E+06 4.00E+06 6.00E+06 8.00E+06 1.00E+07 1.20E+07 1.40E+07

Average Cell Concentration (cells/mL)

Cell

Conc

entr

atio

n (c

ells

/mL)

Image-based Automated Device

0.00E+00

2.00E+06

4.00E+06

6.00E+06

8.00E+06

1.00E+07

1.20E+07

1.40E+07

0.00E+00 2.00E+06 4.00E+06 6.00E+06 8.00E+06 1.00E+07 1.20E+07 1.40E+07

Average Cell Concentration (cells/mL)

Cell

Conc

entr

atio

n (c

ells

/mL)

Image-based Automated Devicey = 0.9574x - 42839 R2 = 0.9762

Manual Hemocytometery = 1.0696x - 138547 R2 = 0.9664

Muse™ Cell Analyzery = 0.973x + 181387 R2 = 0.9826

Manual Hemocytometer Muse™ Cell Analyzer

Image-based Automated Device Manual Hemocytometer Muse™ Cell Analyzer

Image-Based Automated Devicey = 0.9944x - 201508 R2 = 0.9697

Manual Hemocytometer y = 1.0779x - 19232 R2 = 0.9596

Muse™ Cell Analyzery = 0.9277x + 220740 R2 = 0.9942

A. Suspension Lines

B. Adherent Lines

Figure 5. The Muse™ Cell Analyzer provides accurate cell concentration measurements, comparable to results from other analysis methods, for both suspension (A) and adherent (B) cell lines. The plot represents comparison of Muse™ cell concentration data with combined average measured cell concentrations from all three counting methods (Image-Based Automated Device, Manual Hemocytometer and Muse™ Cell Analyzer). Each point represents the average of triplicate measurements.

7

methods, particularly at lower cell concentrations. The data

demonstrated that the Muse™ Cell Analyzer can provide

superior precision for cell counting measurements for

multiple cell lines across multiple concentrations.

Table 3 also demonstrates that the Muse™ Cell Analyzer

has a lower average %CV (2.2%) for viability measurements

compared to the other methods. The %CV for viability

measurements on the Muse™ Cell Analyzer was <7% for

all samples tested.

Figure 6 demonstrates viability results from multiple cell

lines at multiple cell concentrations. Low variation between

viabilities at each concentration was seen, as shown by

the small standard deviation bars. The data support that

the Muse™ Cell Analyzer provides reliable viability results

across a wide concentration range, covering most cell

concentrations encountered during standard culturing

and cellular research.

ConclusionThe Muse™ Cell Analyzer is a multifaceted instrument

that enables measurement of multiple cell health-related

parameters on a single platform. Specific assay modules

facilitate rapid, easy assessment of cell health using assays

for counting and viability (shown in the present study),

apoptosis detection and cell cycle distribution.

Performance data demonstrate excellent correlations with

traditional, accepted analysis methods and confirm that

this new platform yields accurate results for a variety of

cell types and concentrations. Furthermore, the Muse™

platform yields superior precision compared to traditional

methods of cell counting and viability measurement. By

making cell health analysis simple, affordable and easily

accessible, the Muse™ Cell Analyzer can help integrate cell

health analysis into daily cell culture workflows. As a result,

cell-based experiments can be made more consistent and

reproducible, enabling faster, more accurate decisions for

more productive research.

Precision and Reproducibility The precision of Muse™ Count & Viability Assay was evaluated using the analysis methods

and studies described above (Figures 4-5). Table 3 summarizes the average percent coefficient

of variation (%CV) and %CV range obtained using the three methods to analyze 90 cellular

samples from suspension and adherent cell lines.

The table demonstrates that the Muse™ Cell Analyzer provided average %CV of 4.0% for

cellular concentration determination, which was lower than that observed for image-based

automated counting (average %CV of 9.2%) and lower than that observed for manual

hemocytometry (average %CV of 6.3%). While image-based automated counting methods

and manual hemocytometry displayed broader ranges of %CVs, the Muse™ Cell Analyzer

exhibited a narrow range of %CVs and consistently provided %CVs less than 10% over

the entire range of samples tested. Higher %CVs were observed for the Trypan blue-based

Analysis Method

Cell Concentration Viability

Average %CV %CV Range Average %CV %CV Range

Muse™ Cell Analyzer 4.0% 0.3–8.8% 2.2% 0.4–5.6%

Image-based Automated Counter

9.2% 1.2–23.3% 3.7% 0.8–12.1%

Manual Hemocytometer 6.3% 0.5–15.3% 4.5% 0.5–9.2%

Table 3. The Muse™ Cell Analyzer provides superior precision for cell concentration and viability measurements, compared to Trypan blue-based analyses. Data are based on triplicate measurements of 30 cellular samples from suspension and adherent cell lines at multiple concentrations and viabilities.

1.00 E+07 cells/mL

2.5 E+06 cells/mL

1.00 E+06 cells/mL

1.00 E+05 cells/mL

% V

iabi

lity

0

20

40

60

80

100

MCF-7 K562 CHO HB Jurkat

Figure 6. Consistent viability results across various cell concentrations and cell types. Five cell types representing both adherent and suspension lines were harvested and viabilities determined across various cellular concentrations. Each bar represents the average of triplicate samplings and error bars represent their corresponding standard deviations.

Description Catalogue No.

Muse™ Cell Analyzer 0500-3115

Muse™ Count & Viability Kit MCH100102

Muse™ Annexin V & Dead Cell Kit MCH100105

Muse™ Cell Cycle Kit MCH100106

Available from www.millipore.com.

RELATED PRODUCTS

8

Precise and Accurate Bead Counting Using the Scepter™ 2.0 Handheld Automated Cell Counter

Amedeo Cappione, Ph.D., Janet Smith and Kathleen Ongena, Ph.D.EMD Millipore Corporation

IntroductionMicron-sized beads are used in a variety of biological applications, ranging from daily validation of flow cytometer performance to purification of fusion protein constructs from cell lysates. Depending on the nature of the assay, the beads may either possess magnetic properties or be labeled internally with a fluorescent dye. In bead-based multiplexed immunoassays, these particles are coated with unique recognition molecules, such as epitope-specific antibodies, permitting capture and precise quantification of desired analyte(s). Accurate determination of bead counts at the onset of each assay allows for standardization of bead concentrations across multiple samples and minimizes errors and variation in downstream results.

A number of methodologies are currently available for particle counting. While inexpensive, manual counting using a hemocytometer is laborious and error-prone due to user subjectivity. The range of commercially-available automated devices can be divided into two formats: vision-based platforms and flow-based systems. Most vision-based counters use the standard Trypan Blue exclusion assay to assess viability and employ a digital camera and image analysis software to determine particle size and concentration1. Flow-based devices measure particles in a stream using impedance-based detection. By precisely controlling flow, volumetric measurements can be obtained, thereby permitting estimation of sample bead concentrations2.

Table 1. Scepter™ sensor specifications

For most researchers, the main barrier to using an automated vision-based or flow-based system is the price associated with large benchtop instruments3. With the Scepter™ cell counter, EMD Millipore has captured the ease of automated instrumentation and accuracy of impedance-based counting using the Coulter principle in an affordable, handheld format. The instrumentation has been collapsed into a device the size of a pipette, and uses a combination of analog and digital hardware for sensing, signal processing, data storage, and graphical display in the form of a histogram. The 40 µm- and 60 µm-aperture sensors placed at the tip of the instrument are engineered with a microfabricated, sensing zone that enables discrimination by bead size and bead volume at sub-micron and sub-picoliter resolution, respectively. Table 1 outlines the specifications for each sensor type.

While the Scepter™ cell counter was initially optimized for cell counting, here we report that the device is also well suited for precise counting of numerous bead types commonly employed in a wide range of biological applications.

Performance 40 µm aperture sensor 60 µm aperture sensor

Volume required ≥ 100 µL ≥ 100 µL

Particle diameter range 4-16 µm 6-36 µm

Particle concentration 5 x 104 – 1.5 x 106 particles/mL 1 x 104 – 5 x 105 particles/mL

Process time <40 seconds <30 seconds

9

Materials and MethodsBead counting using the Scepter™ cell counterSample preparation:Bead suspensions (see Table 2 for list of beads tested) were

serially diluted in phosphate-buffered saline (PBS, EMD

Millipore Catalogue No. BSS-1006-A). The concentration

range tested, 50,000 - 1,500,000 beads/mL, corresponds

to the upper and lower limits of detection for the 40 µm

sensor. PBS contains an optimal salt concentration for

sufficient conductivity required for optimal counting

performance. For each test, we used the recommended

sample volume of 100 µL in a 1.5 mL microcentrifuge tube.

Other tubes may not be able to accommodate the width

of the sensor, or provide sufficient sample depth for the

instrument to function properly. Since beads settle quickly,

we kept the bead suspension well mixed prior to testing to

ensure reproducible counts.

Scepter™ Bead Counting:Operation of the Scepter™ cell counter is similar to using

a standard laboratory pipette. The Scepter™ cell counter is

turned on by depressing and holding the toggle on the back

of the instrument. Once on, the instrument will prompt the

user to attach a sensor. The Scepter™ unit displays detailed

on-screen instructions for each step of the counting

process. Briefly, depress the plunger and submerge the tip

into the solution. Next, release the plunger to draw 50 µL of

bead suspension into the sensor. The Scepter™ cell counter

detects each particle passing through the sensor’s aperture,

then calculates concentration and displays a histogram of

bead diameter or volume on its screen.

Scepter™ Data Analysis:The upper and lower limits of the histogram, called gates,

are either set automatically based on the histogram profile,

or can be set to the same gates used in the previous count.

After the count is complete and the histogram is displayed

on the instrument, the gates can be moved manually to

fine-tune the analysis. Up to 72 histograms can be stored

on the instrument itself. All test data files can be uploaded

to a computer and further analyzed using Scepter™

Software Pro.

Bead counting by other methodsIn certain cases, counts were also performed using the Z2

Coulter Counter® (Beckman Coulter) and a vision-based

automated cell counter. Counts were performed according

to manufacturer’s instructions using the same starting

suspension and serially diluted samples.

Figure 1. Scepter™ histogram overlays showing serial dilution of two bead types. Scepter™ Software Pro displays imported size distribution histograms as either a single sample histogram or as overlaid histograms for multiple samples. Shown are overlaid histograms for serially diluted 5.6 µm MILLIPLEX® map microspheres and 4.5 µm Invitrogen Dynabeads®.

Table 2. Bead types tested

Bead DescriptionSensor Aperture

Size (µm)Average Measured

diam. (µm) Bead Composition Source Catalogue No.

Latex Microparticles 40 5 Polystyrene Fluka 79633

Innovatis Cedex® Control Beads 40 8 Polystyrene Roche Applied Science 05650542001

Latex Microparticles 40 10 Polystyrene Fluka 72986

MILLIPLEX® map Microspheres 40 6 Polystyrene EMD Millipore MXHIL-4

MILLIPLEX® map Magnetic Microspheres 40 6 Super-paramagnetic EMD Millipore HCYTNFA-MAG

Dynabeads® Cell Isolation 40 4 Super-paramagnetic Invitrogen 11035

PureProteome™ Protein A Beads 40 9 Magnetic EMD Millipore LSKMAGA10

6000

5000

4000

3000Coun

t

2000

1000

063 9

Diameter (µm)

5.6 µm Polystyrene MILLIPLEX® MAP Antibody-Conjugated Microspheres

12 15 18

1,500,000750,000500,000250,000125,00050,000

4.5 m Dynabeads ® for Cell Isolatio n--Superparamagnetic

100,000

8,000

6,000

4,000

Coun

t

2,000

063 9

Diameter (µm)

4.5 µm Dynabeads® for Cell Isolation -Supermagnetic

12 15 18

1,000,000500,000250,000125,00062,500

10

ResultsScepter™ precision countingA variety of bead types ranging in size from 4-10 µm

were counted using the Scepter™ counter fitted with

40 µm sensors. For each bead type, the original sample

was diluted to an approximate concentration of 1.5 x 106

beads/ mL and counted using a Coulter Counter® fitted

with a 50 µm aperture to determine the theoretical starting

concentration. Next, a dilution series was prepared and

assayed to determine theoretical concentrations at each

dilution step. All seven bead types yielded interpretable

histograms that could be gated and used to calculate

sample bead size and concentration. Examples of these

histograms are shown in Figure 1.

In order to validate the reproducibility of measured

concentration values, we plotted the mean derived

concentration values (4 replicates) versus their theoretical

counterparts across the dilution range. The precision of

Scepter™ cell counting was determined by calculating

coefficients of variation at each data point, for each bead

type individually (Figure 2 and Table 3). As shown, the

overlap of data points and small error bars suggest that,

regardless of bead type, concentration values were accurate,

precise, and reliable up to 1.5 x 106 beads/mL. The highest

variability, both within and between bead types, was found

at 1.5 x 106 beads/mL which coincides with the upper limit

of detection for the 40 µm sensor. Overall, the high degree

of linearity (as shown by the R2 values) indicates that

Scepter™ counting is a reliable method for the bead types

tested, across a wide linear operating range.

Figure 2. The Scepter™ cell counter performs with high linearity (R2 ≥ 0.99) across multiple, diverse bead types, over a wide operating range. Shown here are bead concentration data for four representative samples out of seven bead types tested.

Table 3. Precise counting of serial dilutions of 3 bead types using the Scepter™ cell counter.

Theoretical concen-tration (beads/mL)

MILLIPLEX® map antibody-conjugated magnetic beads

PureProteome™ Protein A magnetic beads 10 µm polystyrene beads

Conc. %CV Conc. %CV Conc. %CV

1,500,000 1,650,000 2.7 1,545,667 1.2 1,176,000 8.0

750,000 735,700 3.7 755,767 2.3 n/a n/a

500,000 567,700 2.2 537,933 0.6 514,000 3.2

250,000 300,600 2.8 239,733 3.5 224,467 0.7

125,000 153,367 2.7 131,667 2.6 108,200 2.6

50,000 61,533 2.7 51,980 2.3 39,067 5.6

Figure 3. The Scepter™ cellcounter performs bead counting with smaller coefficients of variation than vision-based automated counting. Shown are the average %CVs (3 bead types: MILLIPLEX® map Antibody-Conjugated Microspheres, MILLIPLEX® map Antibody-Conjugated Magnetic Microspheres and PureProteome™ Protein A Magnetic Beads) with respect to bead concentration and counting method.

Figure 4. The Scepter™ cellcounter counts PureProteome™ Protein A magnetic beads with greater linearity and smaller standard deviation than vision-based automated counting. Beads were counted using the methods shown. Data points represent average of four replicates. Error bars represent standard deviation. These beads represent one out of 7 total bead types tested.

175

Theoretical Bead Concentration (beads/mL x 10,000)

MILLIPLEX® MAP Antibody-Conjugated Micropheres, R2 = 0.998MILLIPLEX® MAP Antibody-Conjugated Magnetic Microspheres,, R2 = 0.995PureProteome™ Protein A Magnetic Beads, R2 = 0.99910 µm Polystyrene Beads, R2 = 0.988Theoretical Concentration (beads/mL)

Scep

ter™

Cel

l Cou

nter

with

40

µm

Sen

sor B

ead

Conc

entr

atio

n(b

eads

/mL

x 10

,000

) 150

125

100

75

50

250

0 25 50 75 100 125 150

35%

Theoretical Bead Concentration (beads/mL x 10,000)

Z2 Coulter Counter® (50 µm aperture)Scepter™ Cell Counter with 40 µm SensorsVision-based System

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Z2 Coulter Counter® (50 µm aperture), R2 = 1.000Scepter™ Cell Counter with 40 µm Sensors, R2 = 0.999Vision-based System, R2 = 0.989Theoretical Concentration (beads/mL)

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Figure 5. The Scepter™ cell counter and the Coulter Counter® accurately measure bead diameter. The average diameters of serially diluted samples of calibrated latex microparticles (5 µm and 10 µm) were measured with the Scepter™ cell counter and Coulter Counter®. Measured diameters were averaged across all samples within the counting range (50000-1500000 beads/mL). Results demonstrate the accuracy of the particle size measurements.

Comparative platform analysisComparing the %CV values for the Scepter™ cell counter,

the Z2 Coulter Counter®, and the vision-based cell counter

acquired during measurement of the serial dilution samples

revealed that the Scepter™ was only slightly less precise

than the gold standard Coulter Counter® but significantly

more precise than the vision–based automated counting

system (Figure 3). Furthermore, the Scepter™ cell counter

data displayed smaller standard deviations than the

vision-based counting system data for all bead types and

concentrations tested (Figure 4).

Size measurementFor the Scepter™ cell counter, the ability to accurately

determine particle size is an inherent property of the

sensor’s aperture. For example, the 40 µm sensor is capable

of sizing particles in the 4-16 µm range. To determine the

accuracy in reporting bead size, we compared our results

to the known bead diameter values (derived from the

certificates of analysis) for 2 bead types. Calibrated latex

microparticles (5 µm and 10 µm) were measured and results

are shown in Figure 5. Accurate sizing was observed using

both the Scepter™ cell counter and the Coulter Counter®.

DiscussionComparing the performance of the Scepter™ cell counter

to results from other counting methods, we conclude that

this new handheld, automated cell counter delivers precise,

fast, and reliable bead counts and bead size measurements

over a wide operating concentration range. The superior

functionality of Scepter™ counting is likely a result of

the precision-engineered technology embedded into the

sensor and the sophisticated counting instrumentation

based upon the Coulter principle. This performance quality,

combined with the Scepter™ cell counter’s convenient,

intuitive form, suggests that Scepter™ counting will be

quickly integrated into the workflow of researchers wishing

to alleviate the pain of rudimentary bead counting and

improve reproducibility of bead-based assays, such as

immunoprecipitation and multiplexed detection.

References1. Tucker KG, Chalder S, al-Rubeai M, Thomas CR. Enzyme Microb

Technol 1994 Jan. 16(1):29-35.2. Houwen, B. Fifty years of hematology innovation: the Coulter

principle. Medical Laboratory Observer 2003 Nov.3. Barghshoon, S. Cell Counting Survey. EMD Millipore 2009 Feb.

15

5 µm 10 µm

Calibrated Particle DiameterScepter™ Cell Counter with 40 µm SensorsZ2 Coulter Counter® (50 µm aperture)

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12

Quantitative Measurement of Autophagy Using a Novel Flow Cytometry AssayMark Santos, Kevin Su, Luke Armstrong, Angelica Olcott, Jason Whalley, and Matthew Hsu EMD Millipore Corporation

AbstractAutophagy is an intracellular catabolic pathway which causes cellular protein and organelle turnover, and is associated with diverse diseases such as Alzheimer’s disease, cancer, and Crohn’s disease, in addition to aging. It is a tightly regulated process that plays a normal part in cell growth, development, and cellular homeostasis. Autophagy functions as a housekeeping mechanism through disposal of aging and dysfunctional proteins and organelles by sequestering and priming them for lysosomal degradation (Figure 1). Increasing evidence suggests that not only

apoptosis, but also autophagy, can contribute to cell death and greatly influence general cell health. Malfunctions of autophagy can adversely impact longevity and the capability of cells to function at full capacity. In cancer cells, autophagy can compensate for hypoxic conditions and nutrient starvation; on the other hand, activation of cell death via autophagy can kill tumor cells. As a result, there is great interest in assays that can efficiently screen for activators and inhibitors of autophagy.

Aging Organelles

Cytosolic Proteinsfor breakdown

Nutrient Depletion

Cytosolic Proteins (eg. LC3)

LC3

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Lysosome

mTor

1. Induction and LC3 translocation

2. Autophagosome formation

3. Docking & fusion with the lysosyme

4. Autophagosome breakdown

PLASMA MEMBRANE

Figure 1. Autophagy maintains cellular homeostasis. Autophagy is a constitutive and dynamic cellular catabolic process required in living cells. Autophagy is responsible for degrading cellular proteins and is currently the only known process for degrading cellular organelles, recycling them to ensure cell survival.

13

Accumulation of Autophagosomes

Selective Permeabilization

SelectivePermeabilization

Types of cellular stress, such as nutrient limitation, hypoxia, oxidative stress, and DNA damage (genotoxic stress), can induce autophagy, often via inhibition of mTOR. Autophagy induction signaling prepares cells to construct a double membrane vesicle known as the autophagosome by catalyzing the scaffolding of Atg proteins (such as LC3) to the pre-autophagosome membrane, which engulfs aging organelles and recyclable proteins. In the final step of autophagy, the outer membrane of autophagosome fuses with the lysosome that provides the hydrolytic enzyme machinery, and the contents are degraded and recycled.

Figure 2. Selective permeabilization aids in discriminating cytosolic from autophagic LC3. Discrimination between cytosolic GFP-LC3-I from autophagosome-associated GFP-LC3-II is achieved by disrupting the plasma membrane with a selective permeabilization solution. Selective permeabilization releases cytosolic LC3, which is then flushed away during washing steps. GFP-LC3-II trapped in the autophagosome remains intact and its fluorescence can be measured.

IntroductionEMD Millipore’s FlowCellect™ GFP-LC3 Reporter Autophagy Assay Kits provide a quantitative solution for studying autophagy and measuring the potency of autophagy inducers using flow cytometry. These kits have four unique features to aid in the detailed evaluation of autophagy by flow cytometry:

Here, we describe a novel flow cytometry assay for studying autophagy by using a GFP-LC3 reporter cell line and a selective permeabilization method. Selective permeabilization enables discrimination between cytosolic GFP-LC3 (LC3-I) and GFP-LC3 which has been lipidated and sequestered into the autophagosomes (LC3-II) as it traffics to the lysosome for degradation. This discrimination is achieved by selectively “flushing out” any cytosolic LC3, while all lipidated LC3 remains “protected” by enclosure within the autophagosome. Because only the protected LC3-II molecules yield a fluorescent signal, the assay enables specific measurement of autophagosomes (Figure 2). The ability to track fluorescently labeled autophagosomes thus provides a tool for measuring autophagy in individual cells.

Figure 3. N-terminally fused (but not C-terminally fused) GFP-LC3 is a valid marker for autophagy. According to Klionsky (2011), the location of GFP fusion to LC3 is critical for measurement of LC3 translocation to serve as a marker for autophagosomes. If GFP is fused to the C-Terminal (or 3’ end), following Atg4 cleavage, GFP is removed and subsequently GFP is lost. But GFP fusion to the N-terminus (or 5’ end) enables retention of GFP, making this construct a suitable marker to track autophagic activity9.

• Selective permeabilization solution discriminates between cytosolic LC3 from autophagic LC3 by extracting the soluble cytosolic proteins, while protecting LC3 which has been sequestered into the autophagosome

• Monomeric GFP is used as a reporter to facilitate the translocation of the fusion protein. Other forms of GFP form dimers and aggregate when overexpressed in the cells, making it difficult to extract from the cytoplasm and impossible to measure translocation by flow cytometry

• TIncluded autophagy detection reagent prevents lysosomal degradation of LC3, allowing its quantification by flow cytometry and prolonging the signaling event for robust measurement.

• The monomeric GFP used in our LC3 fusion protein is attached on the 5’ end (N-terminal fusion), protecting the GFP from Atg4 cleavage, allowing its visualization within the autophagosomes (Figure 3).

C-terminal fusion

Free GFP: not a marker of the phagophore or autophagosome

N-terminal fusion

ATG4

LC3

ATG7

LC3

ATG3

LC3

G-PELC3

G

G*

GR GFP

R GFP

ATG4

LC3

ATG7

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ATG3

LC3

G-PELC3

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GRGFP

GFP

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GFP

GFP-LC3-PE: a marker of the phagophore or autophagosome

14

Our system utilizes a cell line stably expressing GFP-LC3, transfected either into a CHO or U20S cell background. A stable GFP-LC3 reporter system is ideal for screening of autophagy-modulating compounds. LC3 tagged at its N-terminus with GFP allows tracking the translocation of LC3 from the cytosol and into the autophagosome more clearly and definitively than monitoring endogenous protein by antibody-based methods. Especially when endogenous LC3 levels are below the limits of immunodetection, using exogenous LC3 constructs shows a clear benefit. Another advantage of using a stable GFP-LC3 reporter system is the ability to analyze a larger number of cells, because nearly 100% of the population expresses tagged LC3. Analyzing more cells can yield greater statistical significance to experimental observations, for more productive research and better decision-making.

Having the ability to measure compound activity is crucial to screening and rank-ordering compounds in drug discovery campaigns. Here, we have demonstrated the utility of our above-described flow cytometry assay for measuring the autophagy-modulating activity of compounds. First, we show identification of an autophagy inducer (rapamycin) or an autophagy inhibitor (dynasore). Second, we describe the detailed compound analysis and rank-ordering of autophagy inducers (STF-62247 and PI-103) based on the mean fluorescence intensities generated by titration of these known autophagy-inducing compounds. We show quantitative activity measurement for STF-62247 and PI-103 via dose response curves to derive EC50 values.

Using the FlowCellect™ GFP-LC3 Reporter Autophagy Assay on a flow cytometry platform may enable identification of new autophagy targets and pathways, providing insight into aging, cancers, and neurodegenerative disease.

MethodsMonitoring autophagosomes by flow cytometryTo measure autophagy by LC3-II recruitment into the autophagosomes, we used cell lines stably expressing GFP-tagged LC3, transfected either into a Chinese hamster ovary (CHO) or a human osteosarcoma (U20S) cell background. Cells were harvested and placed into a 96-well assay plate, either in nutrient deprived/starved conditions or left in normal fed conditions as a control. Cells were then treated with a lysosomal degradation inhibitor for 2 hours. Cells were then washed with a selective permeabilization buffer at room temperature to extract all cytosolic LC3-I, followed

by one wash with assay buffer to remove any residual permeabilization buffer from the cells. Data were acquired using a guava easyCyte™ flow cytometer to measure the fluorescence signal from autophagosome-bound GFP-LC3-II.

Identification of an autophagy inducer and inhibitor using the FlowCellect™ GFP-LC3 Reporter Autophagy AssayIn order to demonstrate that a GFP reporter-based system is a viable tool for compound hit identification by flow cytometry, GFP-LC3-expressing CHO cells were pretreated with either rapamycin (to induce autophagy) or dynasore (to inhibit autophagy) for 5 hours. A lysosomal degradation inhibitor was also added to the cells simultaneously (if treatment with dynasore) or 45 minutes after rapamycin addition (for a total of 5 hours incubation time) to prevent the autophagosome degradation by the lysosome. After treatment, cells were then washed with a selective permeabilization buffer at room temperature to extract all cytosolic LC3-I, followed by one wash with assay buffer to remove any residual permeabilization buffer from the cells. Data were acquired using a guava easyCyte™ flow cytometer to measure the fluorescence signal from autophagosome-bound GFP-LC3-II.

Small molecule structure-activity relationship (SAR) evaluation using the FlowCellect™ GFP-LC3 Reporter Autophagy AssayThe utility of the GFP-LC3 reporter cell line as a screening tool was further illustrated by measuring the dose-dependent activity of specific small molecule autophagy inducers, STF-62247 and PI-103. STF-62247 and PI-103 were titrated in a 12-point, half-log serial dilution and incubated for 8 hours at 37 °C. A lysosomal degradation inhibitor was also added to the cells approximately 7 hours into the incubation period for 45 minutes to prevent autophagosome degradation by the lysosome. Following treatment, cells were then washed with a selective permeabilization buffer at room temperature to extract all cytosolic LC3-I, followed by one wash with assay buffer to remove any residual permeabilization buffer from the cells. Data were acquired using a guava easyCyte™ flow cytometer to measure the fluorescence signal from autophagosome-bound GFP-LC3-II. The mean fluorescence values, or MFI, were then determined and plotted using a curve-fitting algorithm built into the InCyte™ software module to construct EC50 dose response curves.

15

ResultsBy implementing a selective permeabilization step plus the addition of a lysosomal degradation inhibitor, we were able to measure autophagy via LC3 translocation and subsequent lipidation into autophagosomes by flow cytometry. Combining selective permeabilization with the lysosomal degradation inhibitor was important for developing a suitable platform for screening of molecules for induction or inhibition of autophagy. Figure 4A shows that, without selective permeabilization, the fluorescence signal of the cell population does not change upon autophagy induction, even when a lysosomal degradation inhibitor was added. In contrast, Figure 4B shows that, with selective permeabilization, nutrient deprivation results in an increased localization of LC3 to autophagosome membranes, resulting in a rightward shift in the peak. Nutrient deprivation, together with lysosome inhibition, resulted in an even more dramatic change in the LC3-II signal.

Figure 4. GFP-LC3 stable reporter cell line for detecting the rate of autophagy and for drug screening. In (A), without Selective Permeabilization no shift of GFP-LC3 level is detected using flow cytometry before and after starvation (induction of autophagy). The position of the histograms indicates the high level of GFP-LC3 expression in the cytoplasm.

In (B), with Selective Permeabilization GFP-LC3 level remains high in autophagosomes when starved in the presence of lysosome inhibitor (green); even without the inhibitor, a slight shift is observed when starved (blue). All the cytosolic GFP-LC3 is washed away if no autophagy is induced by starvation (gray).

A. Without Selective Permeabilization

B. With Selective Permeabilization

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Figure 5. Rapamycin induces autophagy, presumably through the mTOR pathway. Rapamycin is an inhibitor of the mTOR pathway, and by targeting mTOR, rapamycin mimics the cellular starvation response and leads to activation of autophagy as illustrated by the right shift of the histogram (green). Cells were treated with 400 nM rapamycin for 48 hours prior to data acquisition.

Figure 6. Dynasore inhibits autophagy by inhibition of autophagosome formation. Dynasore will inhibit autophagosome formation, which in effect, will inhibit autophagy as illustrated by the left shift of the histogram (green) relative to the untreated control (gray). Cells were treated with 80 μM dynasore for 3 hours prior to data acquisition.

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Small molecule analysis #1: Demonstrating the utility of the FlowCellect™ GFP-LC3 Reporter Autophagy Assay for drug screening and structure-activity relationship studiesWe evaluated the effects of the autophagy inducer, rapamycin, and the autophagy inhibitor, dynasore, on GFP-LC3 translocation using the FlowCellect™ GFP-LC3 Reporter Autophagy Assay. Rapamycin is an mTOR inhibitor and induced autophagy in our assay (Figure 5). This observation was consistent with the known role of mTOR in regulating autophagy. mTOR is a member of the PI3-kinase family and is a central modulator of cell growth in response to environmental signals. It plays a critical role in transducing proliferative signals by activating downstream protein kinases that are required for both ribosomal biosynthesis and translations. 2000 Nobel Laureate Paul Greengard demonstrated that a small molecule enhancer of rapamycin, SMER28, decreased levels of amyloid-β (Aβ) peptide, a hallmark of Alzheimer’s disease. Autophagy is one major cellular pathway leading to the removal of such proteins, further suggesting that modulating autophagy can have therapeutic value for Alzheimer’s disease. By targeting mTOR, rapamycin mimics the cellular starvation response by inhibiting signals required for cell cycle progression, cell growth, and proliferation and leads to the activation of autophagy. Dynasore is a cell-permeable inhibitor of dynamin. Dynamin is essential for clathrin-dependent coated vesicle formation. Dynasore acts as a potent inhibitor of endocytic pathways known to depend on dynamin by rapidly blocking coated

vesicle formation within seconds of dynasore addition. As a result, dynasore can prevent autophagosome formation, in turn inhibiting autophagy. Consistent with this model, dynasore inhibited LC3 translocation to autophagosomes in our assay (Figure 6).

16

Small molecule analysis #2: Deep dive evaluation of specific autophagy inducers, STF-62247 and PI-103, by EC50 determination To further investigate the effect of small molecule activity on autophagy, we titrated two well-known autophagy inducers, STF-62247 and PI-103 on our GFP-LC3 reporter cell line, demonstrating that the FlowCellect™ GFP-LC3 Reporter Autophagy Assay was a viable tool for advancing drug candidates. To achieve a quantitative structure-

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Figure 7. PI-103 is a more potent inducer of autophagy than STF-62247.Comprehensive analysis of STF-62247 (A) and PI-103 (B) activity via dose response curves and EC50 determination by flow cytometry using the InCyte™ Software Module illustrated the wide dynamic range of the reporter cell line and validated the effective use of the assay for rank-ordering compounds based on activity.

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96-well Plate Heat map (12 pt, dose response assay) Dose Response Curve

EC50: 2.7 µM

EC50: 1.9 µM

STF-62247

PI-103

A. STF-62247 Dose Response Assay

B. PI-103 Dose Response Assay

activity relationship study, we quantified the level of autophagosome-associated LC3-II by flow cytometry by calculating the mean fluorescence intensity of the compound-treated cells relative to the baseline negative control. From these values, using the InCyte™ software module, we were able to derive EC50 values showing that PI-103 was a more potent inducer of autophagy than STF-62247 (Figure 7).

17

Discussion We have developed an optimized assay protocol for evaluating autophagy by using a GFP-LC3 reporter cell line. By measuring the fluorescence signal of translocated LC3 by a GFP fusion protein, we could detect and measure the rate of cellular autophagy using flow cytometry. This was achieved by using a proprietary selective permeabilization buffer to remove the cytosolic LC3 from cells. Selective permeabilization enabled the discrimination between cytosolic and lipidated LC3, which was sequestered into the autophagosomes. By the addition of a lysosomal degradation inhibitor, we achieved prolonged fluorescence signals, yielding more accurate and reliable data.. We also demonstrated that by using the FlowCellect™ GFP-LC3 Reporter Autophagy Assay, we could successfully characterize compounds that induced autophagy (rapamycin) or inhibited it (dynasore). This novel flow cytometry assay is therefore ideal for screening small molecules for autophagy modulating effects and amenable to high-throughput environments. Moreover, we were also able to successfully implement this method for making SAR determinations by generating EC50 curves and rank-ordering compounds. This was demonstrated by the dose response curves for selective autophagy inducers, STF-62247 and PI-103. By implementing this assay, autophagy-modulating compounds can be rank-ordered to help complement any SAR campaign during drug development, improving and accelerating decision-making during the process of advancing lead compounds further in the development process.

Ultimately, having the ability to accurately measure autophagosome activity via LC3 translocation (which is a hallmark of the autophagic process), as well as perform deep-dive analysis of small molecule activity by deriving dose response curves, can greatly enhance studies of cancer, neurodegeneration, and other diseases affected by autophagy.

References1. Shvets, E., et al. Utilizing flow cytometry to monitor

autophagy in living mammalian cells. Autophagy 2008; 4(5): 621-8.

2. Zhang, L., et al. Small molecule regulators of autophagy identified by an image-based high-throughput screen. Proc Natl Acad Sci U S A 2007;104(48):19023-8.

3. Degtyarev, M., et al. Akt inhibition promotes autophagy and sensitizes PTEN-null tumors to lysosomotropic agents. J Cell Biol. 2008;183(1):101-16.

4. Mizushima, N., et al. Methods for monitoring autophagy. Int J Biochem Cell Biol 2004.;36(12):2491-502.

5. Fleming, A., et al. Chemical modulators of autophagy as biological probes and potential therapeutics. Nat Chem Biol. 2010;7(1):9-17.

6. Tsien, R.. The Green Fluorescent Protein. Annu. Rev. Biochem 1998; 67:509–44.

7. Tian, Y., et al. A small-molecule enhancer of autophagy decreases levels of Aβ and APP-CTF via Atg5-dependent autophagy pathway. FASEB J. 2011;25(6):1934-42.

8. Turcotte, T., et al. A molecule targeting VHL-deficient Renal Cell Carcinoma that induces autophagy. Cancer Cell 2008; 14(1):90-102.

9. Klionsky, D. J., et al. For the last time, it is GFP-Atg8, not Atg8-GFP (and the same goes for LC3). Autophagy 2011; 7(10):1093-4.

Description Catalogue No.

FlowCellect™ GFP-LC3 Reporter Autophagy Assay Kit (CHO), 100 tests FCCH100170

FlowCellect™ GFP-LC3 Reporter Autophagy Assay Kit (U2OS), 100 tests FCCH100181

FlowCellect™ Autophagy Detection Reagent Pack, 100 tests CF200097

Rapamycin 553210

Dynamin Inhibitor I, Dynasore 324410

STF-62247; N-(3-Methylphenyl)-4-(4-pyridinyl)-2-thiazolamine 189497

Available from www.millipore.com.

RELATED PRODUCTS

Guava easyCyte™ benchtop flow cytometers

Description Catalogue No.

High-Throughput Sampling Instruments

guava easyCyte™ 8HT Base System 0500-4008

guava easyCyte™ 6HT/2L Base System 0500-4007

guava easyCyte™ 5HT Base System 0500-4005

PCA-96 Base System 0100-8710

Single Sampling Instruments

easyCyte™ 5 Base System 0500-5006

easyCyte™ 6-2L Base System 0500-5007

easyCyte™ 8 Base System 0500-5008

18

A Novel, Substrate-Agnostic Class III HDAC Activity AssayMichael Sturges1, Haizhen Liu1, Basil Hubbard2, David Sinclair2, Lucas Armstrong1 1EMD Millipore Corporation; 2Paul F. Glenn Laboratories for the Biological Mechanisms of Aging, Harvard University, Boston, MA

IntroductionClass III histone deacetylases (HDACs), also known as sirtuins, are mechanistically distinct from class I and class II HDACs in that they couple deacetylation of the peptide/protein substrate to cleavage of NAD+ to form nicotinamide and O-acetyl-ADP-ribose. The mammalian sirtuins consist of 7 members, termed Sirt1-7, which share the NAD+-binding catalytic domain, but differ in N- and C-termini, subcellular localization, substrate preference,and biological function. Lysine acetylation of histone proteins is controlled by the opposing activities of HATs (histone acetyltransferases) and HDACs. Given the diversity of lysine-acetylated proteins and the biological processes they regulate, modulators of HAT and HDAC enzymes represent potential candidates for drug discovery efforts. Sirtuins became the focus of intense research when it was discovered that their activation led to reduced incidence of aging and age-related diseases such as diabetes1. Initial studies pointed to the small molecule, resveratrol, as a sirtuin activator and potential therapeutic. However, other results demonstrated that unlabeled peptides did not activate sirtuins in a similar manner. These data suggested that the activation observed was due to the use of fluorescently tagged substrates2. Despite these data, multiple lines of evidence are consistent with sirtuins having an impact on health and disease3-6. To better understand the biological roles of sirtuins, researchers would benefit from an alternative assay that uses untagged, native peptide substrates, enabling the study of sirtuins without the complication of fluorophore-mediated activation.

To avoid the pitfalls of fluorescent substrates, measure sirtuin activity in a more physiologically relevant manner and address the diversity of sirtuin isoforms and potential substrates, we developed an assay platform that enables the analysis of sirtuin activity using virtually any appropriate substrate. This new SIRTainty™ class III HDAC assay is a flexible, reliable, homogeneous, no-wash assay for quantifying sirtuin activity. Based upon novel, patent-pending technology, this easy-to-perform assay is coupled to nicotinamidase, which catalyzes breakdown of nicotinamide generated upon cleavage of NAD+ during sirtuin-mediated deacetylation of a substrate. Thus, the SIRTainty™ assay

provides a direct assessment of the activity of class III HDAC enzymes. In contrast to other sirtuin assays that use a fluorescently tagged, acetylated peptide substrate, the SIRTainty™ assay employs an untagged, acetylated peptide substrate.

In this study, we used the SIRTainty™ assay to compare enzymatic activity of three different sirtuins, assess their inhibition by suramin (a previously identified sirtuin-binding molecule7), compare their substrate preferences and, finally, compare the effects of resveratrol with the effects observed using a conventional HDAC assay.

Acetyl-lysine peptide

Lysine peptide

Nicotinamide

O-Ac-ADP-Ribose+

NH3+(free ammonia)

+

NAD+

Nicotinamidase

Sirtuin(class IIIHDAC)

DeveloperReagent

Nicotinic Acid

Ex420 nmEm460 nm

Figure 1. SIRTainty™ Assay Principle. Sirtuin-mediated deacetylation of unlabeled peptide substrate generates nicotinamide as a product. The SIRTainty™ assay couples HDAC activity to nicotinamidase, which cleaves nicotinamide into nicotinic acid and free ammonia. A developer reagent is added, which reacts with the free ammonia to generate a fluorophore. The resulting fluorescent signal is quantified with a conventional fluorometric plate reader.

19

MethodsMeasuring deacetylation activity of three sirtuin family enzymesSirt1 (EMD Millipore Catalogue No. 524743), Sirt2 (EMD Millipore Catalogue No. 524744), and Sirt3 (EMD Millipore Catalogue No. 524745) at a range of concentrations were incubated with 25 µM acetylated peptide (H3K9) substrate and 0.2 mM β-NAD using the SIRTainty™ assay. The Km values were determined by non-linear curve fit of Michaelis-Menten.

Analysis of peptide substrate preference of sirtuin family enzymesPeptide substrates derived from p53, histone H3 and histone H4 (available from EMD Millipore) were selected. Peptides were either unacetylated (H4, H3 and p53), acetylated at one site (e.g. H4K8), or acetylated at more than one site (e.g. H3K9,14). Recombinant Sirt1 (5 Units), Sirt2 (0.7 Unit) and Sirt3 (3 Units) were incubated with indicated acetylated peptide substrates (25 µM) and 1mM β-NAD with the SIRTainty™ assay.

Effectiveness of suramin in inhibiting sirtuin family enzymesRecombinant Sirt1 (5 Units), Sirt2 (0.7 Units) and Sirt3 (1.5 Units) were incubated with 25 µM substrate (H3K9), 0.2 mM β-NAD and suramin and analyzed using the SIRTainty™ assay. Sirt1 exhibited greatest sensitivity to inhibition by suramin.

Effectiveness of resveratrol in activating sirtuin family enzymesSirt1 was incubated with resveratrol at a range of concentrations, and activity assessed with a conventional commercially available kit employing a fluorophore-tagged acetylated peptide substrate, or an untagged acetylated peptide substrate (H3K9) with the SIRTainty™ assay. Resveratrol potentiated Sirt1 activity only with tagged substrate.

ResultsSirtuin isoform activityThe SIRTainty™ assay was effective in measuring activity of all three sirtuin isoforms tested (Figure 2). SIRT2 displayed the highest affinity (Km = 0.14 units) for the acetylated H3K9 substrate used.

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Figure 2. The SIRTainty™ Sirtuin Assay is effective for multiple sirtuin family members.

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Substrate preferenceH3K9, H3K9/14, H4K8, and H4K5/8/12/16 displayed high efficiency for deacetylation as compared to the corresponding non-acetylated peptides for all three sirtuins, as shown in Figure 3. Sirt1 and 2, but not Sirt3, demonstrated higher deacetylation activity with a human p53 peptide acetylated at K382 compared to the non-acetylated peptide.

20

Suramin inhibitionSuramin and suramin analogues typically target purinergic binding sites, which is why they show activity against sirtuins and not other histone deacetylases3. We measured the IC50 of suramin inhibition of three sirtuin isoforms and showed that Sirt1 was most effectively inhibited by suramin (Figure 4).

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Figure 4. Sirt1 shows the greatest sensitivity to inhibition by suramin compared to Sirt2 and Sirt3.

Description Catalogue No.

SIRTainty™ Class III HDAC Assay (1 kit) 17-10090

Calbiochem® Brand Related and Accessory Products

SIRT1, GST-Fusion, Human, Recombinant, E. coli

524743

SIRT2, His•Tag®, Human, Recombinant, E. coli

524744

SIRT3, GST-Fusion, Human, Recombinant, E. coli

524745

Suramin, Sodium Salt 574625

SIRT1/2 Inhibitor IV, Cambinol 566323

Effects of resveratrolConventional HDAC activity assays, which use fluorescently labeled substrates, have been shown to yield misleading results and fluorophore-mediated of sirtuin activity by resveratrol. We showed that, in the SIRTainty™ assay, resveratrol had no effect on Sirt1 activity (Figure 5). In contrast, resveratrol strongly activated Sirt1 activity measured using a conventional assay.

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Figure 5. Unlike a conventional HDAC activity assay with fluorescently tagged substrate, the SIRTainty™ Assay does not permit fluorophore-mediated Sirt1 enzyme activation by resveratrol.

Discussion Our results indicate that SIRTainty™ Class III HDAC Assay offers a convenient, no-wash solution for quantifying sirtuin activity with any untagged acetylated substrate and for easily performing highly sensitive inhibitor assays with multiple sirtuin family members.

The SIRTainty™ assay not only enables unparalleled flexibility in choosing sirtuin isoforms and peptide substrates, but also helps avoid fluorophore-mediated activation observed using other commercial assay formats. Labeled substrates may affect enzyme activity via steric hindrance, electrostatics or other effects. Although there are other methods, such as mass spectrometry, for measuring acetylation status of native, unlabeled substrates, the SIRTainty™ assay is far simpler and enables higher throughput, making it amenable to lead discovery programs and systems-level analyses.

The flexibility and high throughput nature of the SIRTainty™ assay make it ideal for multiple applications, including assaying of peptide substrate panels to determine substrate specificity, screening for activators and inhibitors of sirtuins, comparison of sirtuin family members for profiling and selectivity studies, enzyme kinetics studies, and potency assessment for sirtuin modulators for rank-ordering purposes.

References1. Finkel T. et al. Recent progress in the biology and physiology

of sirtuins. Nature. 2009 Jul 30;460(7255):587-91.2. Pacholec M et al. SRT1720, SRT2183, SRT1460, and resveratrol

are not direct activators of SIRT1. J Biol Chem. 2010 Mar 12;285(11):8340-51.

3. K. T. Howitz et al. Small molecule activators of sirtuins extend Saccharomyces cerevisiae lifespan. Nature. 2003 Sep 11; 425:191.

4. G. Boily et al. SirT1-null mice develop tumors at normal rates but are poorly protected by resveratrol. Oncogene. 2009 Aug 13; 28:2882.

5. J. A. Baur et al. Resveratrol improves health and survival of mice on a high-calorie diet. Nature. 2006 Nov 16; 444:337.

6. R. K. Minor et al. SRT1720 improves survival and healthspan of obese mice. Sci. Rep. 2011; 1.

7. Schuetz A et al. Structural basis of inhibition of the human NAD+-dependent deacetylase SIRT5 by suramin. Structure. 2007 Mar;15(3):377-89.FEATURED PRODUCTS

Available from www.millipore.com.

Description Catalogue No.

SIRT1/2 Inhibitor VII 566327

SIRT1/2 Inhibitor VIII, Salermide 566330

SIRT1 Inhibitor III 566322

SIRT1 Inhibitor IV, (S)-35 566325

SIRT2 Inhibitor, AGK2 566324

SIRT2 Inhibitor II, AK-1 566331

Sirtinol 566320

InSolution™ Sirtinol 566321

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Human PBMC Isolation and Counting Using the Scepter™ 2.0 Handheld Automated Cell CounterAmedeo Cappione, Ph.D.EMD Millipore Corporation

IntroductionPeripheral Blood Mononuclear Cells (PBMCs) are blood cells with round nuclei, such as monocytes and lymphocytes, with the lymphocyte population consisting of T cells, B cells, and natural killer (NK) cells. PBMCs are a critical component of the immune system, playing an integral role in the body’s defense mechanisms. Cellular assays using PBMC cultures form the backbone of immune monitoring studies in clinical diagnostics and therapeutic design. Given that ineffective separation of lymphocytes from whole blood can significantly alter cellular responses and lead to unreliable results, it is essential to start every assay with a rapid, simple, and reliable method of PBMC isolation and subsequent quantitation.

Separation of PBMCs from whole blood is most commonly achieved through density gradient centrifugation using Ficoll® polymer1-3. Differential migration during centrifugation results in the separation of cell types into different layers (Figure 1). The bottom layer contains Ficoll® polymer-aggregated red blood cells. Immediately above this is a diffuse layer containing mostly granulocytes and unbound Ficoll® polymer. Due to a slightly lower density, the lymphocytes (including the monocytic PBMC fraction) sediment at the interface between the Ficoll® polymer and uppermost plasma/platelet layer. PBMCs are removed from the interface and subjected to multiple washes in PBS (or cell medium) to remove any residual Ficoll® polymer. Cell isolates are then ready for analysis or culture setup.

Figure 1. Sample layering before and after Ficoll® polymer density gradient centrifugation.

The Scepter™ cell counter combines the ease of automated instrumentation and the accuracy of impedance-based particle detection using the Coulter principle in a handheld format. Previous work has shown that, by using the new 40 µm aperture sensor, the Scepter™ cell counter was able to accurately and precisely count a much broader range of cell types, including small cells (< 6 µm in diameter) such as PBMC and red blood cells (RBC)4. In this article, we describe successful isolation of PBMC from whole blood and subsequent sample analysis using the Scepter™ cell counter.

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Materials and MethodsWe developed a protocol to fractionate 10 mL of defibrinated or anti-coagulant–treated peripheral blood* (or buffy coat) using 15 mL Ficoll® polymer in a 50 mL conical tube. To ensure high viability of isolated cells, we used only freshly isolated blood samples (< 24 hours after collection). * Anti-coagulants include: Heparin, EDTA, citrate, acid citrate dextrose (ACD), and citrate phosphate dextrose (CPD).

Ficoll® polymer density gradient separation of PBMC1. Transfer 10 mL of blood from the collection vial to

a 50 mL tube.

2. Add an equal volume of PBS (1X EmbryoMax® PBS, EMD Millipore Catalogue No. BSS-1006-A) and mix sample by repeated pipetting

Note: Diluting blood reduces the degree of RBC aggregation as well as freeing trapped PBMC. If left undiluted, trapped PBMC sediment with erythrocytes, reducing yield.

3. Add 15 mL Ficoll® polymer (GE Healthcare) to a second 50 mL tube.

4. Carefully layer the diluted blood over the Ficoll® polymer.

Note: The diluted blood is added to the gradient by gently pipeting onto the separation medium with the tube held at an angle. To obtain good separation, it is paramount that clean separation of the blood and Ficoll® polymer layers is maintained prior to centrifugation

5. Centrifuge without the brake applied at 400 g x 30 min at 18-24 °C.

Note: Higher temperatures (37 °C) enhance RBC aggregation reducing yield while lower temperatures (4 °C) inhibit aggregation, decreasing purity. We recommend centrifuging at 18-24 °C.

6. Carefully remove the tubes from the centrifuge so as to not disturb the layering.

7. Draw off the upper plasma layer being careful not to disturb the lower PBMC interface.

8. Remove the PBMC layer and transfer to a new 50 mL tube. The volume recovered should be approximately 10-12 mL.

Note: Attempt to remove the entire interface while minimizing the amount of Ficoll® polymer or remaining plasma layer. Excess Ficoll® polymer or plasma recovery will result in contamination by granulocytes or platelets and plasma proteins, respectively.

9. Wash PBMC fraction using ~3 volumes of PBS. Centrifuge at 100 g x 10 min at 18-24 °C.

10. Decant the supernatant. Resuspend the pellet in 5 mL PBS. Add PBS to 50 mL and repeat wash step.

Optional: The wash step can be repeated once more

11. Decant the supernatant and resuspend the cell pellet in appropriate volume of PBS (or media)

Notes: 1. From healthy blood, PBMC yield ranges between 0.5-3 x

106 cells per mL blood. For 10 mL blood, resuspend 5 mL PBS for initial count.

2. Purification of the PBMC population may be greatly enhanced by adding RosetteSep® Human Total Lymphocyte Enrichment Cocktail (StemCell Technologies Catalogue No. 15223) after this step.

12. Analyze samples using the Scepter™ cell counter and guava easyCyte™ flow cytometer

13. Samples may require further dilution for accurate counting using the Scepter™ cell counter – the operating cell concentration range for the 40 µm aperture sensor = 5 x 104 – 1.5 x 106.

Scepter™ cell countingThe Scepter™ cell counter was used to count samples following the detailed on-screen instructions for each step of the counting process. After each cell count, the Scepter™ cell counter reported the cell concentration and displayed a size-based histogram as a function of cell diameter or volume on its screen. Scepter™ Software Pro was then used to upload files from the device and perform subsequent data analysis to determine the concentrations and relative cell frequencies for the lymphocyte and monocyte fractions.

guava easyCyte™ cell counting 10 µL of each PBMC sample was diluted in 190 µL PBS. Samples were then analyzed on a guava easyCyte™ HT system to determine the concentrations and relative cell frequencies for the lymphocyte and monocyte fractions.

Cell surface staining and subset determinationFor each sample, 100,000 PBMCs were resuspended in 100 µL PBS+0.1% BSA. To distinguish the discrete cell subsets present in PBMC, samples were stained with the following combination of fluorescently labeled antibodies: anti-CD3-PE (T-cells), anti-CD19-Alexa Fluor® 488 (B-cells), anti-CD16/CD56-APC (NK cells), and anti-CD14- PECy7 (Monocytes) (eBioscience). Samples were incubated at room temperature for 20 minutes, washed with PBS, then resuspended in 200 µL PBS prior to acquisition. Samples were analyzed (3000 cells/sample well) on a guava easyCyte™ HT system using ExpressPro software.

23

ResultsDensity gradient centrifugation using Ficoll® polymer resulted in the characteristic separation of whole blood into 4 distinct layers: an uppermost plasma layer containing platelets, a thin PBMC band, a diffuse Ficoll® polymer layer containing granulocytes, and the aggregated erythrocyte pellet. PBMC could be further subdivided into two main populations, the lymphocytes and monocytes. The lymphocyte subset could be further subdivided into T cells (CD3+), B cells (CD19+), and NK cells (CD16/56+). Monocytes could be distinguished from total lymphocytes on the basis of CD14 expression. Samples were diluted and fractions analyzed using the flow cytometer and Scepter™ cell counter to determine total PBMC concentration. In addition, aliquots of each sample were stained with fluorescent antibodies to the surface markers outlined above.

From the color dot plots in Figure 2, we discerned three event populations found within each sample: debris and red blood cells (Black), a lymphocyte subset (composed of varying numbers of T cells (Red), B cells (Aqua), NK cells (Green)), and a monocyte subset (Blue). While these subsets showed some overlap, each of these three subsets could be clearly defined by the size-based forward-scatter and diameter histogram plots of the flow cytometry and Scepter™ platforms, respectively. That said, there was greater peak distinction in the flow cytometry data than data from Scepter™ counting. Specifically, debris and contaminating erythrocytes each constituted a distinct subpopulation in flow cytometry-derived histograms, but Scepter™ histograms showed only one peak corresponding to both fractions. Nine PBMC samples were analyzed (Table 1).

Across the nine samples, the average mean cell diameters were 7.23±0.30 µm and 10.02±0.20 µm for lymphocytes and monocytes, respectively. Resulting values are consistent with previously reported size ranges5. In addition, total PBMC concentrations were determined by three methods: Scepter™ diameter plot, flow cytometric forward scatter, and antibody staining (Figure 3). Overall, there was good agreement between the different analytical techniques with values varying by <15% in all cases. Small differences in results may be due to subjectivity and user bias in the placement of gates defining the PBMC fraction.

Figure 2. Representative data comparing PBMC samples acquired on the flow cytometer and Scepter™ cell counter. Dot plots show debris and red blood cells (Black), a lymphocyte subset (composed of varying numbers of T cells (Red), B cells (Aqua), NK cells (Green)), and a monocyte subset (Blue).

Table 1. Lymphocyte and monocyte subset frequencies from nine individual PBMC samples. Aliquots from each sample were analyzed using the guava easyCyte™ and Scepter™ platforms. 1Values were derived from the diameter histogram plot. 2Values were derived from the forward scatter histogram plot based on total events measured on guava easyCyte™ platform. 3Staining frequencies derived as follows: %Lymphocytes = (%CD3+ T cells) + (%CD16/56+ NK cells) + (%CD19+ B cells) %Monocytes = %CD14+ cells

Relative FrequencyTest Cell Fraction Scepter™1 Forward Scatter2 Staining3

1 Lymphocyte 58 65 63Monocyte 42 35 37

2 Lymphocyte 68 72 71Monocyte 32 28 29

3 Lymphocyte 66 69 71Monocyte 34 31 29

4 Lymphocyte 62 67 64Monocyte 38 33 36

5 Lymphocyte 64 66 67Monocyte 36 34 33

6 Lymphocyte 62 58 60Monocyte 38 42 40

7 Lymphocyte 65 72 72Monocyte 35 28 28

8 Lymphocyte 59 61 61Monocyte 41 39 39

9 Lymphocyte 64 72 72Monocyte 36 28 28

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Description Quantity Catalogue No.

Scepter™ 2.0 Handheld Automated Cell Counter with 40 µm Scepter™ Sensors (50 Pack) 1 PHCC20040 with 60 µm Scepter™ Sensors (50 Pack) 1 PHCC20060 Includes:

Scepter™ Cell Counter 1

Downloadable Scepter™ Software 1

O-Rings 2

Scepter™ Test Beads 1 PHCCBEADS Scepter™ USB Cable 1 PHCCCABLE

Scepter™ Sensors, 60 µm 50 PHCC60050500 PHCC60500

Scepter™ Sensors, 40 µm 50 PHCC40050500 PHCC40500

Universal Power Adapters 1 PHCCP0WERScepter™ O-Ring Kit, includes 2 O-rings and 1 filter cover 1 PHCC0CLIP

ConclusionPBMCs are widely used in both research and clinical laboratories. Separation of PBMCs from blood by centrifugation constitutes a critical first step for all downstream analyses. We have outlined the basic steps for required for Ficoll® polymer-based purification of mononuclear cells from whole blood and further shown that the Scepter™ cell counter can be used for accurate determination of resulting PBMC counts.

References1. Boyum, A. (1968) Isolation of mononuclear cells and

granulocytes from human blood. Scand. J. Clin. Lab. Invest. 21:77-89.

2. Harris, R. and Ukaijiofo, E. (1970) Tissue typing using a routine one-step lymphocyte separation technique. British J. Haemotol. 18:229-35.

3. Bain, B and Pshyk, K. (1972) Enhanced reactivity in mixed leukocyte cultures after separation of mononuclear cells on Ficoll® polymer-Hypaque. Transplantation Proceedings. 4:161-63.

4. Smith, J and Ongena, K.. The New Scepter™ 2.0 Cell Counter. Cellutions 2011 Vol. 1: p 19-22.).

5. Daniels, V. G., Wheater, P. R., & Burkitt, H. G. (1979). Functional histology: A text and colour atlas. Edinburgh: Churchill Livingstone. ISBN 0-443-01657-7.

6. Smith, J et al. The New Scepter™ 2.0 Cell Counter Enables the Analysis of a Wider Range of Cell Sizes and Types With High Precision. EMD Millipore Cellutions 2011 Vol. 1: p 19-22.

7. Prager, E. et al. (2001) Induction of Hyporesponsiveness and Impaired T Lymphocyte Activation by the CD31 Receptor: Ligand Pathway in T-Cells. J. Immunol. 166: 2364-2371.

Figure 3. Correlation of PBMC concentrations measured using three different methods of analysis: flow cytometric cell counting, Scepter™ cell counting, and flow cytometry analysis of fluorescently labeled cells.

1 Values were derived from the Diameter histogram plot2 Values were derived from the Forward Scatter histogram plot based on total events.3 Population counts derived as follows: %Leukocytes = (%CD3+ T cells) + (%CD16/56+ NK cells) + (%CD19+ B cells); %Monocytes = %CD14+ cells

Available from www.millipore.com.

RELATED PRODUCTS

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3.0E+07

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25

Fortunately, recently developed viral gene delivery vectors, such as lentiviral and adenoviral vectors, permit transduction of virtually any cell type, at more tightly controlled expression levels. Although viral vectors have been utilized successfully for expressing genetically encoded subcellular markers, prepackaged viral vectors have not been widely available, and researchers have had to perform packaging procedures themselves in order to perform subcellular marker analyses.

The LentiBriteTM product family of prepackaged lentiviral particles encode fluorescent fusion proteins with subcellular markers for cell fate, cytoskeletal structures and adhesion. The lentivirus particles are packaged with 3rd generation lentiviral packaging plasmids, which produce pseudoviral particles that have vanishingly low probabilities of pathogenicity2. The fluorescent proteins employed are TagGFP2 and TagRFP, which have been demonstrated to be monomeric for minimal interference with the function of the fusion partner proteins and have quantum yields comparable to fluorescent proteins from other species3,4.

In this article, we highlight biosensors for visualizing autophagy, in which TagGFP2 and TagRFP are fused at their C-termini to the autophagosome marker LC3. LC3 precursors, diffusely distributed in the cytosol, are proteolytically processed to form LC3-I. Upon initiation of autophagy, the C-terminal glycine is modified by addition of a phosphatidylethanolamine to form LC3-II, which translocates rapidly to nascent autophagosomes in a punctate distribution5. DNA constructs encoding fluorescent proteins fused to LC3 are widely employed for introduction into cells for monitoring autophagosome formation by fluorescence microscopy. EMD Millipore’s LentiBrite™ GFP-LC3, GFP-LC3 Control Mutant, and RFP-LC3 lentiviral particles provide bright fluorescence and precise localization of LC3 to the autophagosome, enabling live cell analysis of

autophagy even in difficult-to-transfect cell types.

LentiBrite™ Lentiviral Biosensors for Fluorescent Cellular Imaging: Analysis of Autophagosome FormationJanet Anderl, Karyn Huryn-Selvar, Haizhen Liu, Kevin Su, Mark Santos, Matthew Hsu, Jun Ma and Luke ArmstrongEMD Millipore Corporation

AbstractExpression of genetically-encoded fluorescently-tagged proteins has widely been employed for real-time visualization of cellular behavior and trafficking. Prepackaged, ready-to-use, high-titer lentiviral particles (which we have termed “lentiviral biosensors”) encoding GFP- or RFP-tagged proteins are a convenient, robust solution for fluorescent imaging of transduced cells. Compared to other nonviral transfection methods, lentiviral transduction, in many cases, offers higher transfection efficiency and more homogeneous protein expression, particularly for traditionally hard-to-transfect primary cell types. Lentiviral biosensors are ideal for use with fixed and live cell fluorescent microscopy, and are non-disruptive towards cellular function. GFP- or RFP-protein localization matches well with antibody-based immunostaining and demonstrates altered patterns of expression upon treatment with modulators of cell function and phenotype. Lentiviral biosensors provide a broadly effective, convenient method for visualization of cell behavior under a variety of physiological and pathological treatment conditions, in both endpoint and real-time imaging modalities. In this study, we focus on lentiviral biosensors containing GFP-LC3 and RFP-LC3 for the study of autophagosome formation.

IntroductionAnalysis of the dynamics of subcellular structures has been revolutionized in the past 15 years by the development and refinement of genetically-encoded fusions between fluorescent proteins and cellular structural proteins. Such fusion proteins, if designed properly, incorporate into the structure of interest without disturbing its function, and permit visualization of the structure in live cells and in real time by fluorescence microscopy1. The cDNAs encoding the fusion proteins have traditionally been delivered into cells by chemical transfection or electroporation. However, such transient transfection procedures have drawbacks, including highly variable expression levels and low efficiencies for transfecting primary cells. Selection of clonal cell lines stably expressing the construct of interest allows for optimized expression levels, but the process is time-consuming and is not feasible for primary cells.

26

Materials and MethodsConstruction of lentiviral vectors encoding fluorescent protein fusionsLentiBriteTM GFP-LC3, RFP-LC3, and GFP-LC3 Control Mutant Lentiviral Biosensors were constructed as follows: the cDNAs encoding TagGFP2 and TagRFP were obtained (Evrogen) and the cDNA encoding human LC3A residues 1-120, which represents the proteolytically processed, mature form of LC3A, was cloned in-frame at the 3’ end of the fluorescent protein cDNA. The resulting fusion proteins, TagGFP2-LC3 and TagRFP-LC3, leave the C-terminal glycine (Gly120) of LC3 available for lipidation upon induction of autophagy. To generate a control mutant that does not translocate upon induction of autophagy, site-directed mutagenesis was employed to mutate LC3 Gly120 to alanine, which renders the protein refractory to lipidation. Constructs were transferred to pCDH-EF1-MCS (System Biosciences Inc.), a lentiviral vector containing the constitutive, moderately expressing EF1α promoter. 3rd generation HIV-based VSV-G pseudotyped lentiviral particles were generated using the pPACKH1 Lentivector Packaging System from System Biosciences.

Cell Seeding and Lentiviral TransductionCells in growth medium were seeded onto 8-well glass chamber slides for fixed cell imaging, or chambered cover glasses for live cell imaging. Seeding densities were selected to provide for 50-70% confluency after overnight culture (e.g., 20,000-40,000 cells/cm2). The next day after seeding, medium was replaced with fresh growth medium. High-titer lentiviral stock was diluted 1:40 with growth medium, and lentiviral volume was added to the seeded cells for the desired multiplicity of infection (MOI). MOI refers to the ratio of the number of infectious lentiviral particles to the number of cells being infected. Typical MOI values ranged from 10 to 40. Infected cells were then incubated at 37 °C, 5% CO2 for 24 hours. 24 hours after lentiviral transduction, lentivirus-containing medium was removed and replaced with fresh growth medium. All lentivirus-containing medium and plasticware in direct viral contact were disinfected with 10% bleach before disposal. Cells were cultured for another 24-48 hours, changing medium every 24 hours. For autophagy experiments, cells were either left in growth medium or incubated in Earle’s balanced salt solution (EBSS) containing a lysosomal inhibitor for 2-4 hours. In some cases, 5 mM 3-methyladenine (3-MA) was included to inhibit autophagosome formation.

Live Cell ImagingFor live cell visualization, the chambered cover glass was placed in a temperature-controlled microscope stage insert. Imaging was initiated as rapidly as possible following addition of modulator. LC3-expressing cells were imaged in EBSS containing a lysosomal inhibitor. Live cell imaging was performed upon a Leica DMI6000B inverted wide-field fluorescent microscope with a 63X oil-immersion objective lens and illumination/filters appropriate for GFP or RFP visualization.

Cell Fixation, Staining and ImagingCells were fixed for 30 minutes at room temperature with 3.7% formaldehyde in Dulbecco’s phosphate-buffered saline (DPBS). During fixation and for all subsequent steps, cells were protected from light to minimize photobleaching. Samples were then rinsed twice with fluorescent staining buffer (DPBS with 2% blocking serum and 0.25% Triton X-100). For immunocolocalization studies, primary antibody in fluorescent staining buffer was added to each well for 1 hour incubation at room temperature. Samples were then rinsed three times with fluorescent staining buffer before proceeding on to 1 hour room temperature incubation with fluorescent secondary antibody and DAPI (1 µg/mL) in staining buffer. Finally, samples were rinsed twice with fluorescent staining buffer and DPBS, and slides were coverslipped with mounting media containing anti-fade reagent and No. 0 cover glasses (Ted Pella). Mounted specimens were imaged on inverted wide-field (as above) or Leica DMI4000B confocal fluorescent microscopes, utilizing illumination and filters appropriate for GFP, RFP, Cy5 (for immunocolocalization), or DAPI excitation and emission wavelengths. Imaging was performed with a 63X

oil-immersion objective lens unless otherwise indicated.

Analysis of GFP-LC3 localization by flow cytometryThe LentiBrite™ Autophagosome Enrichment Kit was

employed for analysis of autophagosome formation in

primary cells. Human umbilical vein endothelial cells

(HUVEC) were incubated with lentivirus encoding TagGFP2-LC3 or TagGFP2-LC3G120A (control mutant) at an MOI of 40 for 24 hrs. After removal of the lentivirus, the cells were cultured for an additional 48 hrs. Cells were either left in complete growth medium or incubated in EBSS containing a lysosomal inhibitor, and were subsequently detached with Accutase™ and permeabilized. U2OS cells stably expressing TagGFP2-LC3 (FlowCellect™ GFP-LC3 Reporter Autophagy Assay Kit (U2OS), were treated in parallel as a positive control. Samples were analyzed immediately on a guava easyCyte™ 8HT flow cytometer. Data were analyzed with the InCyte™ Software Module.

27

Results and DiscussionPlasmid transfection vs. lentivirus transductionUsing LentiBrite™ lentiviral biosensors, we demonstrated the improved efficiency of gene delivery and homogeneity of gene expression achieved by lentiviral transduction. In Figure 1, easily transfectable HeLa cells were transfected with GFP-labeled tubulin via plasmid (with a chemical transfection reagent) or via lentivirus. Lentivirally transduced cells demonstrated higher transfection efficiency (percentage of cells positive for signal, compared to total number of

cells) compared to chemically transfected cells, as well as more homogeneous expression (compared to the range of high and low expressers in the plasmid-transfected population). For a typically “hard-to-transfect” primary cell type such as human umbilical vein endothelial cells (HUVEC), lentiviral transduction produced homogeneously bright signal in a significant proportion of cells, in contrast to plasmid transfection, which resulted in minimal GFP-tubulin expression.

HeLa (easy-to-transfect) HUVEC (difficult-to-transfect)

Figure 1. Plasmid vs. lentivirus transfection in easy- and hard-to-transfect cell types. HeLa cells and HUVECs were transfected with a TagGFP2-tubulin-encoding construct, either utilizing plasmid DNA in conjunction with a lipid-based chemical transfection reagent, or using LentiBrite™ lentiviral particles. Images were obtained via wide-field fluorescent imaging with a 20X objective lens (blue = DAPI nuclear counterstain, green = GFP-tubulin). Lentiviral transduction resulted in higher transfection efficiency (particularly for HUVEC, for which plasmid transfection was unsuccessful) and GFP-tubulin signal of more uniform fluorescence intensity.

Specificity of localization of lentivirally delivered GFP-LC3Genetically-encoded biosensors for studying autophagy have become a widely employed technique since the first description of the use of GFP-tagged LC3 to detect autophagosome formation5. Although immunofluorescent detection of endogenous LC3 can be performed, genetically encoded biosensors achieve greater sensitivity for detecting changes in autophagosome formation. However, the use of transient transfection of plasmid DNA for expression of GFP-LC3 has been criticized for causing artifactual, autophagy-independent punctae, and the preferred approach is to use cell lines stably expressing the GFP-LC3

construct6. To determine whether lentiviral delivery of DNA encoding fluorescent proteins fused to LC3 avoids such artifacts, we produced lentivirus encoding TagGFP2-LC3 for transduction into a broad variety of cell types. We found that lentiviral delivery of fluorescent protein-tagged LC3 allowed for accurate detection of autophagosome formation, as determined by 1) immunofluorescent co-localization of LC3, 2) live cell imaging of autophagosomeformation, 3) use of a mutant version of LC3 that is resistant to lipidation and fails to localize to autophagosomes, and 4) use of autophagy inhibitor.

DAPIGFP-tubulin

GFP-tubulin

DAPIGFP-tubulin

GFP-tubulin

Chemical transfection

Lentiviral transduction

28

To compare localization patterns of genetically-encoded fluorescent proteins with antibody-based immunofluorescence, HeLa cells were lentivirally transduced with GFP-LC3. GFP-LC3-expressing cells were either left untreated, or subjected to starvation conditions to induce autophagy by incubation in EBSS. A lysosomal inhibitor was also included to prevent degradation of LC3-containing autophagosomes. Both the fluorescent protein and anti-LC3 antibody displayed diffuse nuclear and cytoplasmic signal under fed conditions and a punctate distribution following starvation (Figure 2).

Next, we analyzed time-dependent LC3 translocation following autophagic induction in live cells. By wide-field microscopy, lentivirally-transduced cells were imaged every minute over the course of approximately 2 hours following treatment. Full length video is available at www.millipore.com/autophagyvideo. Autophagy was induced in GFP-LC3-transduced HT-1080 cells via EBSS/lysosomal inhibitor starvation, resulting in accumulation of punctate LC3 in newly formed autophagosomes. As shown in Figure 3, significant formation of autophagosomes was visible at 50 minutes. At 110 minutes, nearly all of the visible GFP signal was localized to autophagosomes.

For an additional assessment of the specificity of the GFP-LC3 biosensor, we employed two controls: a mutant LC3 that is resistant to lipidation, and an autophagy inhibitor. In Figure 4, cells were lentivirally transduced with TagGFP2-LC3 or a TagGFP2-LC3 non-translocating control mutant. Transduced cells were starved in EBSS with a lysosomal inhibitor, in the presence or absence of 3-methyladenine, an inhibitor of PI3 kinase that blocks autophagosome formation. The mutant LC3 fusion protein did not translocate to a punctate cytoplasmic distribution upon starvation. Also, when starved in the presence of 3-methyladenine, both the wild-type and mutant LC3 fusion proteins displayed a diffuse distribution throughout the nucleus and cytoplasm, as typical of fed cells.

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Figure 2. GFP-LC3 fluorescent signal (green) colocalizes with signal from fluorescent staining using LC3 antibody (red). HeLa cells were-transduced with TagGFP2-LC3, and 72 hrs later, either left in growth medium or starved for 4 hours in EBSS with a lysosomal inhibitor. Cells were subsequently fixed, immunostained, and imaged by wide-field microscopy. Starved, autophagic cells displayed punctate cytoplasmic LC3 distribution, in contrast to diffuse nuclear and cytoplasmic localization under fed conditions. Fluorescently tagged protein co-localized with staining obtained with anti-LC3.

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Figure 3. Live cell time-lapse imaging of lentivirally-transduced cells. HT-1080 cells were lentivirally transduced with TagGFP2-LC3, and imaged by oil-immersion wide-field microscopy in real-time. The cells were starved in the presence of a lysosome inhibitor, and imaging was immediately initiated, with images obtained every minute for 2 hours. Still-frame captures demonstrate formation of GFP-LC3-positive discrete cytoplasmic punctae.

Figure 4. Autophagy inhibitor 3-MA prevents translocation of GFP-LC3. HT-1080 cells were lentivirally-transduced with TagGFP2-LC3 wild-type (GFP-LC3 wt) or TagGFP2-LC3G120A (GFP-LC3 mutant) at MOI of 20. Transduced cells were either left in growth medium, or starved in EBSS with lysosome inhibitor in the presence or absence of 3-methyladenine (3-MA). Cells were fixed, mounted, and imaged by wide-field fluorescence microscopy. Cells transduced with wild-type GFP-LC3 no longer exhibited cytoplasmic punctae under starvation conditions in the presence of 3-methyladenine. In addition, cells transduced with a negative control mutant GFP-LC3 maintained diffuse nuclear and cytosolic distribution under all conditions.

Anti-LC3 Anti-LC3 Anti-LC3

GFP-LC3 wt GFP-LC3 wt

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29

Analysis of autophagy in difficult-to-transfect cell types by imaging and flow cytometryPrimary cell types, including HUVEC and human mesenchymal stem cells (HuMSC) are traditionally considered difficult-to-transfect cell types for plasmid DNA-based chemical transfection. LentiBrite™ lentiviral transduction is shown to be capable of inducing fluorescent protein expression in these cell types in Figure 5. Both HUVEC and HuMSC were successfully transduced at high efficiency with TagGFP2-LC3 and TagRFP-LC3. As seen with the lentivirally transduced HT-1080 cell line in the previous figures, the fluorescent protein fusions in the primary cells expressed diffusely when cultured in growth medium, and adopted a punctate distribution following starvation in the presence of a lysosome inhibitor.

To more accurately assess the extent of LC3 reporter redistribution in primary cells, we employed a flow cytometry assay in which the plasma membrane is selectively permeabilized such that free cytosolic fluorescent protein-tagged LC3 is released while autophagosome-bound LC3 fusion protein is retained. HUVECs were lentivirally transduced with TagGFP2-LC3 or TagGFP2-LC3G120A (control mutant). The cells were subsequently starved of amino acids in the presence of a lysosome inhibitor or left untreated, then detached and either permeabilized (using the LentiBrite™ Autophagosome Enrichment Kit) or left intact.

Upon permeabilization, the GFP-LC3 in starved cells was almost completely retained, but was greatly depleted in fed cells (Figure 6). This result was similar to the pattern observed in U2OS cells stably expressing TagGFP2-LC3 (FlowCellect™ GFP-LC3 Reporter Autophagy Assay Kit (U2OS)). In contrast, permeabilization caused a large reduction in TagGFP2-LC3G120A in both starved and fed cells. In U2OS cells transiently transfected with plasmid encoding TagGFP2-LC3, a very broad distribution of fluorescence was observed, and the shift upon permeabilization of fed cells was much less pronounced (data not shown).

RFP-LC3

Figure 5. Lentiviral transduction enables analysis of autophagy in hard-to-transfect primary cell types. HUVEC and HuMSC were lentivirally transduced at an MOI of 40 with TagGFP2-LC3 or TagRFP-LC3, and fed or starved as in Figure 2. Cells were then fixed and imaged by wide-field microscopy. The transduced fluorescent proteins displayed diffuse distribution in growth media and a punctate distribution following starvation-induced autophagy.

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Figure 6. Analysis of GFP-LC3 localization in HUVEC by flow cytometry. HUVECs were lentivirally transduced with TagGFP2-LC3 wild-type (GFP-LC3 wt, top row) or TagGFP2-LC3G120A control mutant (GFP-LC3 mutant, center row). U2OS cells stably expressing TagGFP2-LC3 wild-type were also analyzed (U2OS-GFP-LC3, bottom row). Transduced cells were detached and either permeabilized to release free, cytosolic LC3 (green peaks) or left intact (gray peaks). After processing, the cells were analyzed by flow cytometry on a guava easyCyte™ 8HT instrument. Upon permeabilization, only TagGFP2-LC3 wild-type-expressing cells under starvation conditions display retention of the fusion protein, indicative of tight association of LC3 with autophagosomes.

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30

ConclusionsLentiBrite™ lentiviral biosensors enable convenient transduction of easy- and hard-to-transfect cell types with fluorescently-tagged proteins of interest. These pre-packaged lentiviral particles provided higher efficiency of gene delivery and more homogeneous expression of introduced proteins compared to non-viral transfection methods. The data presented here demonstrates the utility of a GFP- or RFP-tagged autophagy marker, LC3, in both fixed and live cell microscopy applications. The encoded proteins displayed co-localization with antibody staining and appropriate redistribution upon treatment with known modulators of autophagy. Other available constructs in the LentiBrite™ portfolio target foundational proteins associated with apoptosis, cell structure and adhesion: calreticulin, α-tubulin, β-actin, vimentin, α-actinin and paxillin. LentiBrite™ biosensors provide a ready-to-use solution for researchers seeking to fluorescently visualize the presence/absence or trafficking of a protein, under normal, abnormal, diseased, or induced cellular states.

References1. Goldman, R. D., Swedlow, J. R., and Spector D.L. (Editors).

Live Cell Imaging: A Laboratory Manual, 2nd ed., 2009; Cold Spring Harbor Laboratory Press, Cold Spring Harbor, NY.

2. Pauwels, K. et al. State-of-the-art lentiviral vectors for research use: risk assessment and biosafety recommendations. Curr. Gene Ther. 2009; 9: 459-474.

3. Mertzlyak, E.M. et al. Bright monomeric red fluorescent protein with an extended fluorescence lifetime. Nat. Methods 2007; 4: 555-557

4. Subach O.M. et al. Conversion of red fluorescent protein into a bright blue probe. Chem. Biol. 2008; 15: 1116-1124.

5. Kabeya, Y. et al. LC3, a mammalian homologue of yeast Apg8p, is localized in autophagosome membranes after processing. EMBO J. 2000; 21: 5720-5728.

6. Klionsky, D.J. et al. Guidelines for the use and interpretation of assays for monioring autophagy in higher eukaryotes. Autophagy. 2008; 4: 151-175.

Description Catalogue No.

LentiBrite™ GFP-LC3 Lentiviral Biosensor 17-10193

LentiBrite™ RFP-LC3 Lentiviral Biosensor 17-10143

LentiBrite™ GFP-LC3 Control Mutant Lentiviral Biosensor 17-10189

LentiBrite™ RFP-LC3 Control Mutant Lentiviral Biosensor 17-10188

LentiBrite™ Autophagosome Enrichment Kit 17-10230

LentiBrite™ GFP-Tubulin Lentiviral Biosensor 17-10206

LentiBrite™ RFP-Tubulin Lentiviral Biosensor 17-10205

LentiBrite™ GFP-β-Actin Lentiviral Biosensor 17-10204

LentiBrite™ RFP-β-Actin Lentiviral Biosensor 17-10203

LentiBrite™ GFP-Vimentin Lentiviral Biosensor 17-10152

LentiBrite™ RFP-Vimentin Lentiviral Biosensor 17-10153

LentiBrite™ Calreticulin-RFP-KDEL 17-10146

LentiBrite™ Paxillin-GFP 17-10154

LentiBrite™ α-actinin-RFP 17-10196

FlowCellect™ GFP-LC3 Reporter Autophagy Assay Kit (CHO) FCCH100170

FlowCellect™ GFP-LC3 Reporter Autophagy Assay Kit (U2OS) FCCH100181

Autophagy Inhibitor, 3-MA (3-Methyladenine) 189490

Millicell® EZ slide (4-well) PEZGS0416

Millicell® EZ slide (8-well) PEZGS0816

DAPI, Dihydrochloride 268298

EndoGRO™ Human Umbilical Vein Endothelial Cells (HUVEC) SCCE001

Human Mesenchymal Stem Cell Kit (Derived from Bone-Marrow) SCR108

Polybrene Infection/Transfection Reagent TR-1003-G

Available from www.millipore.com.

RELATED PRODUCTS

31

Innovative detection solutions for autophagy LentiBrite™ Lentiviral Biosensors

Visualize autophagy in real time with pre-packaged lentiviral

biosensors, even in difficult-to-transfect primary cell types. Reproducible

and convenient, these new LentiBrite™ GFP- and RFP-LC3 lentiviral particles

reveal precise localization of LC3 during autophagosome formation.

LentiBrite™ Biosensor Advantages:

• Robust – Long-term, stable fluorescent expression of GFP- or RFP-LC3

• Efficient – High transduction efficiency, non-disruptive to cell function, low immunogenicity

• Easy interpretation – LC3 Control Mutant lentiviral biosensors contain

the translocation-defective protein LC3-G120A for negative comparison

HT-1080 cells after transduction with LentiBrite™ GFP-LC3 Biosensor (Catalogue No. 17-10193). GFP-LC3 displays diffuse nuclear and cytosolic distribution in fed cells (A), and punctate distribution in starved, autophagic cells (B).

PRODUCT HIGHLIGHT

Description Catalogue No.

LentiBrite™ GFP-LC3 Lentiviral Biosensor 17-10193

LentiBrite™ GFP-LC3 Control Mutant Lentiviral Biosensor 17-10189

LentiBrite™ RFP-LC3 Lentiviral Biosensor 17-10143

LentiBrite™ RFP-LC3 Control Mutant Lentiviral Biosensor 17-10188

Get 20% off any LentiBrite™ Lentiviral BiosensorMention promotion code ZBIOS445 Offer valid until 30 June 2012.

Read more about LentiBrite™ autophagy assays in the research paper on page 25 of this issue.

Scan this QR code to watch a video of autophagy occuring in real time or visit www.millipore.com/autophagyvideo.

A B

EMD Millipore, the M mark, Scepter, LentiBrite, Muse, PureProteome, FlowCellect, easyCyte, InCyte, SIRTainty, InSolution, EndoGRO are trademarks and Millipore, MILLIPLEX, Calbiochem, His•Tag, EmbryoMax, and Millicell are registered trademarks of Merck KGaA, Darmstadt, Germany. Coulter Counter is a registered trademark of Beckman Coulter. Excel is a registered trademark of Microsoft. Dynabeads is a registered trademark of Invitrogen. Cedex is a registered trademark of Roche Diagnostics GMBH. Ficoll is a registered trademark of GE Healthcare. RosetteSep is a registered trademark of Stem Cell Sciences. Alexa Fluor is a registered trademark of Molecular Probes. Lit No. PR1205EN00 BS-GEN-11-04628 1/2012 © 2012 EMD Millipore Corporation, Billerica, MA, USA. All rights reserved.

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