Flow Cytometry webinars

Our flow cytometry webinars are designed to give a basic framework for learning experimental setup, compensation, and data analysis. These webinars also provide tips and tricks from our experienced technical support scientists and offer guidance for selecting the best products to help you achieve optimal results.

Recorded flow cytometry webinars

How recent technological advances in flow cytometry instrumentation are enabling faster throughput

This webinar presented by J. Paul Robinson at Purdue University, will focus on advances in the Invitrogen Attune NxT Flow Cytometer and outline where the technology within the Attune NxT fits in current and future applications.

Multiparameter Cell Cycle Analysis

Find out how to get better cell recovery and definition of cell cycle compartments. This webinar will focus on multiparametric cell cycle analysis of DNA and specific epitopes using a “washless” staining assay to minimize sample handling.

0:00:00 - 0:00:39, Slide 1

Moderator: Hello, everyone. Welcome to today’s live broadcast, Multiparameter Cell Cycle Analysis, presented by Dr. James Jacobberger, professor emeritus, and director of cytometry and microscopy core, Case Western Reserve University. I am Alexis Cross of LabRoots, and I’ll be your moderator for today’s event. Today’s webinar is part of the protein and cell analysis education series, brought to you by LabRoots and sponsored by Thermo Fisher Scientific. For more information on our sponsor, please visit thermofisher.com.

0:00:39 - 0:01:13

Now, let’s get started. Before we begin, I would like to remind everyone that this event is interactive. We encourage you to participate by submitting as many questions as you want at any time you want during the presentation. To do so, simply type them into the Ask a Question box, and click on the Send button. We’ll answer as many question as we have time for at the end of the presentation. If you have trouble seeing or hearing the presentation, click on the Support tab found at the top right of the presentation window, or report your problem by clicking on the Ask a Question box located on the far left of your screen.

0:01:13 - 0:01:32

This presentation is educational and thus offers continuing education credits. Please click on the Continuing Education Credits tab located at the top right of the presentation window and follow the process to obtain your credit. So, without further ado, Dr. Jacobberger, you may now begin your presentation.

0:01:40 - 0:03:06, Slide 2

Thank you, Alexis. Hello, everyone.
The term cell cycle analysis has come to define cytometric assays to count cells and cell cycle phases, compartments, or states. I’ll use these words throughout the talk interchangeably. This slide shows a single parameter histogram in the upper left for cell stain for DNA content, which gives us three phases, G1, S, and G2+M. A two-parameter plot in the lower left shows cells stained for DNA content plus a mitotic marker which then gives us four phases, G1, S, G2, and M. M is synonymous with the mitotic phase - stages of the cell cycle. G1 are cells with one genome. S are cells that are synthesizing a second genome. G2 and M are cells with two genomes.

On the right, a three-parameter plot shows cells stained for DNA content, a mitotic marker, and two cyclin proteins. These are proteins that oscillate periodically and regulate the cell cycle. These data create a multi-compartment model of contiguous obligate states that cells pass through during the cell cycle. That is multiparametric cell cycle analysis.

0:03:06 - 0:04:55, Slide 3

In general, DNA is the primary base marker. It provides a three-compartment subdivision ordered in time, G1, then S, then G2+M. Periodically expressed genes or activities subdivide these phases further with the goal to create a model with objectively defined compartments that are unambiguous, that is, the cells pass through each compartment unidirectionally once.

This slide shows two parameter histograms of data synthesized from the expression profiles of such periodic expression. The upper row contains expression that rises and plateaus within a single phase. The middle shows expressions that rise across phases. In both rows, cells divide at the plateau level, creating daughter cells that have half that level. In the third and fourth rows, expression rises and declines within a single phase, for example, the first, second, and fourth columns, or across phase boundaries, the third column. These are synthesized patterns based on ideas, but we and others have observed, most of these patterns when looking at a specific marker.

In many cases, the compartments derived from these patterns are not unambiguous. For example, mitotic markers show elevated expression and mark mitotic cells, but the pattern contains cells that are entering and exiting mitosis within the same data space, i.e. that compartment is ambiguous. To render this ambiguous compartment unambiguous, we need additional markers.

0:04:55 - 0:06:18, Slide 4

I now introduce a fixation entertaining protocol that is extensively published, so the specifics are unimportant, other than the lengthy time it takes to prepare cells prior to cytometry. Key elements are formaldehyde to fix the cells and prevent further enzymatic activity, followed by alcoholic denaturation, and permeabilization, or detergent permeabilization without denaturation. Different markers are optimized by variations in this basic protocol. Since markers are intercellular, optimized staining requires time, which is 30 to 90 minutes, depending on the desired data quality, with 90 minutes providing the highest signal-to-noise ratio. Further, after antibody incubation, three washes at 15 minutes each, plus centrifuge time are optimal.

In the next few slides, I’ll present for K562 cells, a human erythroid leukemia cell line, stained for DNA content, (phospho S10) Histone H3, which is a mitotic marker, and two mitotic cyclins, A2 and B1. We’ll walk through the resulting data analysis step by step to create a multi-compartment cell cycle analysis.

0:06:18 - 0:07:40, Slide 5

This slide illustrates the first analysis steps, which clean up the data. These include doublet discrimination, exclusion of any anomalies from perturbed flow, and a correction for signal drift. A region is set that includes singlet cells based on the shape of the primary DNA signal pulse. The first plot shows the signal peak on the Y-axis and the signal area on the X. Doublets, triplets, and so on have peak heights equal to single cells that areas equal to multiples of single cells. The middle row shows plots of DNA content versus time. For this run, there are no areas of perturbed flow, but if there were, regions would be set - used to exclude incorrectly measured data.

However, although here the effect is small, there is a continuous decrease in the signal over time. That can be corrected in the same way as compensation is applied. The quality, that is, the CV of the data, does not change over time, so the correction improves the CV of the corrected data compared to uncorrected data. We examine all parameters for this effect, and corrected the overall effect of that’s severe enough. This doesn’t always happen, and the causes behind it are complex and still under study.

0:07:40 - 0:08:30, Slide 6

Since we are going to move continuously through a multiparameter data space through bivariate windows, I’ve included this slide to illustrate the overall process. What is plotted isn’t important because in the following slides, we will go through the process step by step. What is important is the idea that we have moved through. In this case, five parameter data space, following the arrows in a manner that does not exclude any combination that would equal a cell cycle state, or at least that is the idea. In this example, we start at the box labeled Begin and end where the box labeled End, which is the beginning and end of the cell cycle.

0:08:30 - 0:09:43, Slide 7

The next step is to separate interphase from mitosis. That is shown in the panel on the left. Phospho-Histone H3 and many other heavily phosphorylated epitopes increase dramatically when cells enter mitosis. For this epitope and others, dephosphorylation occurs in late mitosis and continues through immediate early G1. Thus, we can segment all M by gating all 4C cells with elevated phospho-Histone H3.

In these same plots of phospho-Histone H3 versus DNA, we can capture the early G1 cells, shown by the arrow and the word “Newborn.” In these same plots of phospho-Histone H3 versus DNA, we can capture the early G1 cells. We can check that measurement by examining cyclin B1 versus Histone H3. Because of the decreased background of the small early G1 cells, we can separate early G1 from the rare late M cells that have partially dephosphorylated Histone H3. That’s shown in the right panel.

0:09:43 - 0:10:42, Slide 8:

The next steps are to isolate G1 proper, that is, minus early G1. Both G1 and G2 can be segmented using cyclin A2 versus DNA plots with M cells removed by Boolean logic. The remaining cells are in S. The critical G1S and SG2 boundaries can be checked by comparing the G1S and G2 phase fractions of the DNA distribution determined by Gaussian modeling of DNA content with programs such as ModFit to the frequencies of cells determined by color coding logic. Because there is a shape to the cyclin A2 versus DNA S phase component, it can be broken into early S, S1, and the remainder of S, S2. The plot on the far right shows the DNA distribution color coded for G1, S1, S2, and G2.

0:10:42 - 0:11:56, Slide 9:

Next, we segment G1 into two states, G1A and G1B. The importance of segmenting G1 into two states, an uncommitted G1A and committed G1B separates what used to be called the restriction point, which is a commitment checkpoint. The mitotic cyclones are repressed in the uncommitted state via activity of the anaphase promoting complex/cyclosome or the APC/C. As it is inactivated, cyclone B1 expression increases. The level is still low and expression is continuous, therefore, it is not a great marker, but at present, some information can be obtained without adding another marker. The left figure shows how we can use the G1S boundary. S1 is color coded maroon, defined previously, and the early G1 cells here are colored black to determine approximately where to place the region boundaries. The panel on the right shows the progression of the states G1A, G1B, S1, S2, and G2 in terms of cyclin B1 levels.

0:11:56 - 0:15:05, Slide 10:

We next turn to M phase. We define the earliest M state P1 as poor C-cells with maximum levels of cyclins and rising levels of phospho-Histone H3. That is shown in the upper left on the plot of pHH3 versus cyclin A2. The previously defined G2 cells in green define the critical boundary, and the upper boundary is placed at the cluster edge. Thereafter, gating only on M cells, we plot cyclin B1 versus cyclin A2, and the pattern shown in the upper right is segmented at cluster boundaries. In this plot, P1 and P2 are coincident, and P2 is defined by Boolean logic that gates - that excludes P1.

When the APC/C begins to activate, cyclin A2 is degraded, and this is captured as PM because cells in this state correlate with prometaphase. When cells have reached undetectable levels of cyclin A2, the mitotic checkpoint is entered with stable high levels of cyclin B1 and depleted cyclin A2. This state is labeled M because it normally correlates with metaphase. If cells are treated with a mitotic spindle inhibitor, for example, nocodazole, they arrest here and this state will become highly populated.

Next, cyclin B1 is degraded and this correlates with the onset of anaphase, but the rate of decay does not correlate well with the sequence anaphase I, anaphase II, and telophase. Since these states do not correlate well with morphologically defined stages, we label them LM for late mitosis. LM1 is defined by cells with less than maximum but more than minimum cyclin B1, that is, cells that are degrading cyclin B1. LM2 is defined by cells when cyclin B1 has been degraded to a minimum.

Within this state, there are additional states that can be defined by morphology. On a plot of DNA pulse peak versus the DNA integrated signal for phospho-Histone H3-positive cells, we observed two clusters in the transition. The reason for this is that if the cytometer is well-tuned, the late anaphase and telophase cells look like doublets. This is shown in the lower right panel. The cells with the lowest peak signal, that is, those enriched in telophase can be further subdivided into a group with maximum phospho-Histone H3, that is, LM2C, and those with lower levels, LM2D.

The figure on the lower left shows that cells divide from LM2C and LM2D, or at least that is the working hypothesis.

0:15:05 - 0:15:43, Slide 11:

Thus, in a 3D plot, we can visualize most of the 15 unambiguous states, 14 of which are sequentially traversed by unperturbed proliferating cells. An average cell of this population under the proliferative conditions defined by the environment at the time of fixation moves sequentially from G1MB to LM2C, then optionally move to G1MB or LM2D, then to G1MB. This provides a continuous backbone onto which other markers can be mapped.

0:15:43 - 0:16:45, Slide 12:

Now, I’d like to introduce the idea of adding information obtained from a different platform. The data are from a laser scanning cytometer. In this case, these are a different attached cell line, but in principle, the same samples of fixed and stained cells that were analyzed by the flow cytometer previously could be analyzed in this manner.

The plot at the upper left are cells stained for phospho-Histone H3 and DNA, and I have set a gate for mitotic cells. The plot on the lower left shows nuclear size on the Y-axis, and on the X-axis, the density or cyclin B1 in each cell. Region R10 equals the largest nucleus with the minimum cyclin B1 density. Region R15 equals the smallest 4C nucleus with maximum cyclin B1 density.

0:16:45 - 0:17:15, Slide 13

If we look at the R10 images, we observe that cyclin B1 is cytoplasmic and on center zones, but not in the nucleus. Many of the center zones are well separated. At the beginning of mitosis, cyclin B1 first accumulates on the center zone, which then begins to separate and each row end up opposite each other, and create the poles of the mitotic spindle.

0:17:15 - 0:17:34, Slide 14

If we look at the images in R13 to the right of R10, we can see that cyclin B1 has entered the nucleus for approximately 50% of the cells. In those cells in which cyclin B1 is still on the cytoplasm, there are two center zones, and most are well separated.

0:17:34 - 0:17:54, Slide 15

If we go to R15, the cells are almost all on metaphase. Cyclin B1 is now cytoplasmic again because the nuclear membrane has broken down. Although, the cyclin B1 density is still high because the cells have rounded up and cyclin B1 decorates the mitotic spindle.

0:17:54 - 0:19:59, Slide 16

Now, I’d like to take a step back and go over the underlying principle behind the complex bivariate data patterns we’ve been analyzing. This slide shows the expression of two parameters over time. The top row is parameter one and the middle row is parameter two. The data are event cells that vary on the Y-axis, that is, for every point in time, there are multiple cells that represent the expression level if all of the cells in the population were synchronous. The two parameters are correlated in time. When a proliferating population of cells are sampled, because they are asynchronous, all points along the timeline of expression are represented. Thus, if we plot parameter one versus parameter two, we get two-parameter histograms, as shown in the bottom row that look like a typical flow cytometry data.

I’ve color coded the clusters and transitioned bottom row and their corresponding segments in the two top rows. For example, the two stable minima at the beginning and the ends of the time sequence are ochre and brown. They are represented in the bottom bivariate plots in the first cluster in the lower left. The sharply rising expression of parameter one, cyan, is correlated with expression of parameter two at a stable minimum, also cyan. Therefore, the transitional cyan cells move, “to the right in the parameter one direction and remain fixed in the parameter two direction in the bottom of the histogram.” If the expression of one parameter shifted in time relative to the other parameter, the number of self-populating clusters in transition is affected, middle and right columns.

0:19:59 - 0:21:34, Slide 17

Now, going back to our data from the laser scanning cytometer. If we calculate the median fluorescence and plot it on the Y-axis, and plot relative cell cycle time calculated from the compartment frequencies on the X-axis, we can see the profiles underlying the parameters. Here, phospho-Histone H3 expression and cyclin B1 density or any other marker that we have included. If we have imaging data, then we can do the same thing for other information by showing the time-related cumulative change in frequencies.

For example, this plot shows the parameter expression profiles, and reveals the variation and entry or passage through the mitotic stages. It can be seen that accumulation of two center zones parallel prophase, and both correlate with the rise in phospho-Histone H3. Equally, cyclin B1 enters the nucleus rapidly in all of the cells over a very short period, and this is followed immediately by entry to prometaphase. The expression of cyclin B1 through this period is maximal and stable, but the density sharply rises at the end of prophase. Metaphase is more variable, but by the time most of the cells have entered, cyclin B1 density remains high. By the time anaphase begins, cyclin B1 density is falling. Thus, we can quantitatively correlate expression movement of molecules and morphological changes.

0:21:34 - 0:24:38, Slide 18

This is about where we and others have taken multiparametric cell cycle analysis. I’d like to make a few comments, and then I’ll present some of our recent work. Since I can imagine critics saying that, “This is all nice but what real value is this complicated analysis,” I’ll start on the right side of this slide first. The first practical application is in pharmacodynamics. Cell cycle regulators and other targets that affect the cell cycle are still rational approaches in cancer chemotherapy. This type of an assay might provide improved information in pharmacodynamics studies and subsequently as a possible therapeutic guide. We have built an analysis of DNMT1, a target of azacitidine or decitabine therapies using a reduced version of this approach.

Second, for any blood or bone marrow analysis, some subset of this approach could reduce the complexity of data by normalizing expression of other molecules across the cell cycle, possibly reducing the weather data by a factor of two and coincidentally providing high quality doublet discrimination. Finally, my good friend, David Hedley, believes that it is time to use this information to study the effects of drugs in the therapeutic studies of using xenografts. I believe he’s right.

To the left side, these are the things that I would do going forward. G1 can be compartmentalized just as we have done for mitosis. There are many candidate markers, the data will be a little more messy because G1 is more highly variable, but I expect it to break apart in a similar fashion. G2 has a major DNA damage checkpoint operating. This has been extensively studied and the probes are available to see if G2 could be as interesting. I would like to do studies that incorporate data from multiple platforms into a single correlated dataset using the principles I’ve outlined, imaging is my favorite, but slit scan is underexplored, and the work of [George DuCane] and [Sergei Gulnik] demonstrates that differential permeable evasion may be a fruitful way to get at molecular sequestration.

I think the day has come for a new generation of probes, and camel antibodies are my favorite in that regard. Multiparametric data should be analyzed by probability state modeling. See Bruce Bagwell’s worked on this. This would eliminate or reduce significantly the number of decisions about where to set regions. Finally, I think improved hardware is not out of the question.

0:24:38 - 0:26:09, Slide 19

Speaking of improve hardware, I’d like to present some results that I’m very pleased with.

Here’s a reference to a paper by Goddard et al. on acoustic focusing cytometry. This is a technology where acoustic energy is directed into the flow cell in such a way that the center of the string is a volume where the waves cancel and there is an energy absence. Thus, the cells move to the center and once in the center, they stay there. This is illustrated in the next few images by an illustration on the left and a photograph on the right.

Thermo Fisher has commercialized this technology in the Attune NxT Flow Cytometer. What this meant to us is that we could try to develop a semi-washless protocol and reduce the time that we use to stain cells. The idea is to wash away the fixative normally, and then incubate in antibodies in a small volume, then add one large dilution. The Attune instrument can run a large volume so rapidly, up to 1 mil per minute. The instrument can do this because the cell wall move to the center and remain in focus. Thus, the unwashed large volume sample might be able to go directly on the instrument after an incubation period in a dilution.

0:26:09 - 0:26:50, Slide 20

Here is the protocol. Without going through the entire thing, it is just like our standard protocol except we save time on the washes and acquisition time with a time savings of two hours, 5 ½ hours compared to 3 ½ hours. Additionally, in these assays, each centrifugation results in a loss of cells. To the extent that starting with a million cells, usually provides significantly less than 100,000 analyzable events. We have not quantified it, but this washless assay does provide a noticeably better cell recovery. We’ll now look at some comparative data.

0:26:50 - 0:27:15, Slide 21

Here are the data comparing the two assays. They are visually equivalent. The top row compares phospho-Histone H3 versus cyclin A2. The middle row compares phospho-Histone H3 versus cyclin B1. The bottom rows compares cyclin B1 versus cyclin A2 from mitotic cells.

0:27:15 - 0:27:25, Slide 22

We quantified five mitotic states as these are the rarest events. The regions are shown in the bottom row.

0:27:25 - 0:27:50, Slide 23

Here is the result. There’s not a significant difference between the percentages of cells within each compartment or the levels of cyclin A2 and B1 in the cells. Therefore, this is a significant step forward for this kind of work. Since I have made the comments that I would ordinarily say for the summary, I’ll stop here.

0:27:50 - 0:28:15, Slide 24

One more thing, the work shown in this talk depended on the work of many people over many years from my own laboratory and those of many others. The references listed on this slide are reviews of the work or the extensive references to our work and that of others. Thank you for listening.

0:28:15 - 0:28:50, Slide 25

The people who did the work at Case Western Reserve University are Tammy Stefan and Phil Woost on the experimental side, Mike Sramkoski and Allison Kipling on the instrument side. From Thermo Fisher, Suzanne Schloemann ran our initial experiments, and Brian Wortham provided access to the instrument. Jolene Bradford and Mike Ward were critical for moving this project forward, and we continue to work with them on other projects.

0:28:50 - 0:29:17, Slide 26

Moderator: Thank you, Dr. Jacobberger, for your informative presentation. We will now start the live Q&A portion of the webinar. If you have a question you would like to ask, please do so now. Just type them into the Ask a Question box, and click on the Send button. We’ll answer as many questions as we have time for at the end of the presentation. Questions we do not have time for today will be answered by Dr. Jacobberger via email following the presentation.

0:29:17 - 0:30:35Question 1

Our first question is, presumably information increases with the addition of parameters, is the relationship simple? If so, what is the relationship between information and parameter number?

I’ve looked at this in some detail and the short answer is that there’s no simple relationship. I have the feeling that we hit on early on with parameters that were more informative per parameter than I expect to get per parameter in the future. For sure, an additional parameter should add at least one piece of information or it’s redundant. It’s probably not unthinkable that two or three pieces of information might be added per parameter. That’s about all I know about that.

0:30:35 - 0:31:15, Question 2

Our next question is, you have presented that analytical scheme of the backbone with the idea that this is definitely extensible. Have you tested this idea?

The short answer is no, I haven’t. There’s no reason that I can possibly think of that would prevent that from being true. So theoretically, it’s doable, but a real test of it is still – needs to be done.

0:31:15 - 0:33:55, Question 3

It looks like we have time for one more question. Can you give some examples of when a no-wash or minimal wash protocol will be effective?

Yes, I think that - as far as I’m concerned, it’s effective almost always because it saves time, but I think the, perhaps, more important case would be for things like clinical samples that are very limited in cell numbers. So the idea of them not losing cells through centrifugation comes to the forefront, and the idea of incubating with antibodies and then just diluting up and running, and being able to run almost the entire sample is very appealing especially for clinical samples.

We did a study here recently, that was published recently, on the sickle-cell disease and treatment of patients with low-dose decitabine to reactivate fetal hemoglobin. What we were looking for was the decrease in DNMT1 as a function of the decitabine dose. All of the samples had been done prior to us developing the assay, and we didn’t have any input into how the samples were prepared. Those samples were – I don’t remember the exact cell numbers, but it was very few cells, something like a couple of 100,000 in a relatively large volume of fixative. So precious samples that - where we really didn’t want to lose any cells. In that case, the no-wash approach is almost a lifesaver. It’s not that it can’t be done with washing, but I’m certainly much happier with the no-wash solution.

0:33:55 - 0:34:27, Question 4

Thank you again, Dr. Jacobberger. Do you have any final comments for our audience?

No, I hope you enjoyed the seminar – or the webinar. My email is, I think, in the credits or whatever, and I’m happy to answer any questions, if somebody has them, after the fact through email.

0:34:27 - 0:34:40

Moderator: I would like to once again thank Dr. Jacobberger for his presentation. I would also like to thank LabRoots and Thermo Fisher Scientific for making today’s education webcast possible. Questions we did not have time for today will be answered by Dr. Jacobberger via email following this presentation.

0:34:40 - 0:35:03

Before we go, I’d like to remind everyone that today’s webcast will be available for on-demand viewing through May 30th of 2018. You will receive an email from LabRoots alerting you when this webcast is available for replay. We encourage you to share that email with your colleagues who may have missed today’s live event. That’s all for now. Thank you for joining us, and we hope to see you again soon. Goodbye!

Making polychromatic flow cytometry easy after instrument characterization and verification

Join Grace Chojnowski, Flow Cytometry and Imaging Facility Manager at QIMR Berghofer Medical Research Institute as she discusses how instrument standardization can help simplify your flow cytometry.

0:00:0 - 00:00:14, Slide 1

Hello. We’re glad you’ve joined us in this live webinar, “Making Polychromatic Flow Cytometry Easy After Instrument Characterization and Validation”. I am Judy O’Rourke of LabRoots and I’ll be moderating this session.

0:00:14 - 0:00:57

Today’s educational web seminar is presented by LabRoots, the leading scientific social networking website and provider of virtual events and webinars advancing scientific collaboration and learning. It’s brought to you by Thermo Fisher Scientific. Thermo Fisher is a world leader in serving science whose mission is to enable customers to make the world healthier, cleaner, and safer. Thermo Fisher Scientific helps their customers accelerate life sciences research, solve complex analytical challenges, improve patient diagnostics and increase laboratory productivity. For more information, please visit www.thermoscientific.com.

0:00:57 - 0:01:51

Let’s get started. You can post questions to the speaker during the presentation while they freshen your mind. To do so, simply type into the dropdown box located on the far left of you screen labeled “Ask a question” and click on the Send button. Questions will be answered after the presentation. To enlarge the slide window, click on the arrows at the top right-hand corner of the presentation window. If you experience technical problems seeing or hearing the presentation, just click on the support tab found at the top right of the presentation window or report your problem by typing it into the “Answer a question” box located on the far left of your screen. This is an educational webinar and thus offers free continuing education credits. Please click on the “Continuing education credits” tab located at the top right of the presentation window and follow the process of obtaining your credits.

0:01:57 - 0:02:51

I now present today’s speaker, Grace Chojnowski, the Flow Cytometry and Imaging Facility Manager at the QIMR Berghofer Medical Institute in Brisbane, Australia. Grace has a strong instrumentation background and has been managing flow cytometry core facilities for more than 30 years. She came to QIMR Berghofer in 1993 having earlier worked as the flow cytometry facility manager at the Peter MacCallum Cancer Institute in Melbourne. She graduated from RMIT University with a Master’s in Applied Science. Grace is active with the Australasian Cytometry Society organizing courses and meetings and serves on the executive committee. She has served two terms as counselor for the active International Society for Advancement of Cytometry and continues to be active with the society. Grace’s complete bio is found on the LabRoots website. Grace Chojnowski will now begin her presentation.

0:02:51 - 0:03:15

Thank you very much, Judy, and thank you for your kind words and the opportunity to share the work we’ve done here at QIMR Berghofer. We’ve kept for instrument characterization and validations.

0:03:15 - 0:04:13, Slide 2

So, my role at QIMR Berghofer is to try and help to facilitate good cytometry to all our internal academic users, our external academic users and also helps some of our commercial clients and contract work as well as some of the clinical child work we do. We have users who have got a reasonably deep understanding of flow cytometry but then we also have those who become a little bit overwhelmed by the complexity of some of the instrumentation. We have users who prepare a panel and an analyzer then want to do some sorting using the same panel and cannot understand why their populations may look slightly different and why one instrument gives them better separation than others.

So, we embarked on this sort of experiments to see if we can add some of those questions.

0:04:13 - 0:04:41, Slide 4

So, what did we do? Beads are the standard use for flow cytometry performance checking. We know that these beads use different dyes to the ones that are commonly used when performing flow cytometry applications and assays. As such, we wanted to test our instruments response to the same fluorochrome that we use for our day-to-day assays and not just the dyes that are attached to the beads.

0:04:41 - 0:06:00

Another point is that the brightness or staining intakes that’s provided by vendors give us an indication of how bright some these fluorochromes may be, but that is not always the same on our instruments, on my instrument or your instrument. We saw 61 CD4 antibodies with 46 fluorochromes and a number of different clones. It would’ve been ideal to have had the same clone for all the different fluorochromes, but we weren’t able to obtain all 46 in the same clone. Initially, we started using normal healthy volunteer peripheral blood, but as you can imagine, we needed a lot of blood from one volunteer so we could avoid any variability from different multiple donors in one experiment. After a while, we decided to start using comp beads as well. The reason is that comp beads don’t change from day-to-day or individual-to-individual. The concentration is always constant and it’s a lot easier to use comp beads than large volumes of blood from different volunteers.

0:06:00 - 0:06:58, Slide 5

So, polychromatic flow cytometry has grown rapidly over the last five to 10 years. You know, many years ago we had the organic dyes like FITC and some of the phycoproteins like PE and APC, then along came some of the tandem dyes, two-dots and more recently the new Sirigen dyes. This has increased the availability of new dyes along with new instrumentations that’s able to acquire many more parameters simultaneously allowing us to measure so many more antigens simultaneously on one single cell. Having a good handle on instrument characteristics the optical configuration can have a big impact on the ability to resolve many different cell populations.

0:06:58 - 0:07:36, Slide 6

Many vendors that do sell reagents with the different fluorophones will give us an indication of their level of brightness and some vendors will rank these dyes as can be seen in this table, but this ranking maybe different on your instrument or my instrument either because of my optical configuration is different, or it can use different collection optics, my laser, excitation power or wavelength may be different as well.

0:07:36 - 0:07:50, Slide 7

Here we have some fluorochromes that are ranked in order of brightness but also grouped in either high brightness, medium or low.

0:07:50 - 0:08:05, Slide 8

Here, again, we’ve got the rank from very high to very low or also numerically from a five to a one low.

0:08:05 - 0:08:57, Slide 9

Here’s the list of the fluorochromes that we used in our instrument characterization as well as well as the CD4 clone that was associated with that fluorochrome. There were three main vendors that we obtained our antibodies from and I’ll name them A, B, and C. The CD4 Clone SK3 was the most common one used, followed by the RPA-T4, and then a couple of the S3.5 clones. You can also see that with some of the antibodies, the same clone with that with the same fluorochrome was available from a number of different vendors. For example, APC we could get for a number of the vendors.

0:08:57 - 0:10:06, Slide 10

This table shows us the optical configuration of the instruments at our institute which we used for these experiments and which we obtained our individual instrument characteristics on. You can see that some of the instruments have got identical optical configuration such as the Fortessa 1 and 2 are identical. The Fortessa 4 and 5 are also identical, so they have the same lasers, the same dichroics, the same collection filters whereas the other ones may have slight variations with some the bandpass filters and also dichroics. I haven’t listed their dichroic filters or the steering optics here but they may also differ slightly from instruments to instrument.

0:10:06 - 0:11:13, Slide 11

Okay. So antibody titer, all antibodies are tittered for both the normal healthy volunteer or the peripheral blood but we also did compare titers using the comp beads. We expressed the titer as protein in micrograms per mil. We wanted to make sure that the same amount of antibody is present from all the antibodies that we tested and the only difference was the fluorochrome that we were using. We wanted to also make sure that once you have a titer with your peripheral blood. That’s a lot easier to obtain, but with comp beads, we really wanted to make sure that the amount the amount of protein, because of all the binding sites on comp beads, we wanted to make sure that the protein between all the fluorochromes was just the same for the beads.

0:11:13 - 0:11:42, Slide 12

The staining index. What is the staining index and what does the staining index tell us? It lets us know how well-resolved a population is from the background or from the negative population. The brighter the signal above the background or the unstained in the sample, the higher the staining index.

0:11:42 - 0:13:01, Slide 13

For all the data required for each antibody from each titer, we calculated the staining index. You calculate the main fluorescence intensity or the MFI of the positive cell population, the MFI of the negative cell population, then calculate the staining standard deviation of the negative population, and then you give a formula where you subtract the negative MFI from the positive MFI and divide it by the two times the standard deviation of the negative population. As the negative population expands or the staining index the standard deviation will also increase which will then have an effect on the staining index. The staining index allow us to determine how well resolved our CD4 population is from the background or the negative population.

0:13:01 - 0:14:17, Slide 14

Here is an example of some of the staining index results we got and what we considered was the optimal titer. So, we did this for all the fluorochromes. I’ve also plotted the positive medium fluorescence and the negative medium fluorescence, and then in red we can see the staining index. You could also see that sometimes if a population - if you’ve got the highest mean fluorescence intensity on a positive population, that doesn’t necessarily mean that that higher titer or that concentration is going to give you the best staining index so sometimes you will get an increase in the negative which will have an effect on the total staining index or that final staining index number. It’s about getting the best separation between your negative cells and your positive cells.

0:14:17 - 0:15:13, Slide 15

Here is an example of some of the staining index results we got and what we considered was the optimal titer. So, we did this for all the fluorochromes. I’ve also plotted the positive medium fluorescence and the negative medium fluorescence, and then in red we can see the staining index. You could also see that sometimes if a population - if you’ve got the highest mean fluorescence intensity on a positive population, that doesn’t necessarily mean that that higher titer or that concentration is going to give you the best staining index so sometimes you will get an increase in the negative which will have an effect on the total staining index or that final staining index number. It’s about getting the best separation between your negative cells and your positive cells.

0:15:13 - 0:15:46, Slide 16

We also did some voltration experiments where we altered the voltage for each detector just to see what was the best voltage for the different fluorochromes and looked at the range where you do get a better signal between your negative and your positive cells.

0:15:46 - 0:17:00, Slide 17

The results. Here, we’ve got the response from one of our instruments, one of our Fortessas. It had five lasers and we’re able to configure the instrument to acquire data from all the fluorochromes we tested. So, we can see here that the two fluorochromes that give us the best staining index are from two different vendors. They’re excited by the same laser, collected from the same detector and also these two fluorochromes have got similar vector properties. What this is indicating is that the detectors that are picking up those fluorochromes are very sensitive to that spectrum and also those two fluorochromes are reasonably bright as well.

0:17:00 - 0:18:20, Slide 18

Here we’ve got four instruments. The two top instruments are identical in their optical configuration and the bottom two are also identical with their optical configuration. The two on the left are older instruments. The SH-04 and the F4A are older instruments and the two on the right have been purchased more recently. The two on the left were very popular. They’re usually quite booked out and they use a slight reconfiguration, they like to run their experiments, and they said, “Well, we need instruments that are exactly the same.” So, we purchased additional instrument with the same configuration to keep our users happy. We can see that the older instruments don’t have as good as response as the new ones. Later on in this presentation, I will try and address why this is, or why I think this is.

0:18:20 - 0:18:38, Slide 19

Here we have one of their cell sorters and you can see that the response to most of the fluorochrome is very good but the response to the fluorochromes excited by the 561 laser are very good.

0:18:38 - 0:19:10

Just before we perform these instrument performing characteristics. See, we had a few issues with the instruments, and the 561 laser died. So, we got a brand new laser, all the fibers, everything had been replaced and everything had been realigned which could be another reason why the yellow grain is giving us much superior signals to the rest fluorochromes.

0:19:10 - 0:20:00, Slide 20

This is the stream-in-air cell sorter. It’s out to MoFlo and, again, we’ve got an excellent response to the yellow green fluorochrome. One thing that we did do for this instrument when the new yellow - the new fluorescent protein dyes became popular, we purchased a reasonably high-powered laser to excite some of those fluorescent protein such as mCherry. So, the power from this laser is a lot, lot higher than the power on the other lasers on the instruments. It also can explain why we’re getting such a good response.

0:20:00 - 0:20:55, Slide 21

So, do the same fluorochromes have better response on our instruments from the different vendors? No. Not really. They’re all pretty much the same. We compared the same CD4 clone with the same fluorochrome from different vendors and found that they pretty much gave similar results on all our different instruments.

We’ve got the APC, FITC, PE-Cy7 and PE and all the instruments and the responses.

They all were where you’ve got a better response for one fluorochrome you also got a reasonably good response from the other vendors as well.

0:20:55 - 0:21:59, Slide 22

Comparisons between different fluorochromes but where you’re using the same detector. When you compare different fluorochromes from different vendors, there are differences. These could be due to the actual brightness of the dye but it could also be due to the specific collection optics of that instrument. You know, some filters maybe better suited for a particular fluorochrome than another and we can’t constantly be changing our collection filters to accommodate from one fluorochrome from one experiment to another. You can see that some of the emission spectra are reasonably close but they’re all not identical.

0:21:59 - 0:24:21, Slide 23

In an ideal flow cytometry world, our dyes would’ve made a narrow spectral band with no or very little spillover signal into different detectors. Reality is, fluorochromes emit into a whole lot of other detectors where you don’t want them to either to adjacent detectors but also to other detectors where you’ve got different excitation wide length. Sometimes the spillover can be quite significant and this will make multicolor or polychromatic cytometry and panel design difficult. When there’s spillover of this sort of fluorescents into multiple detectors, it will have an effect for a spring, the nominal negative cell distribution, and this can have a huge impact on the ability to resolve some populations especially those that have got a very low antigen expression. So, what we did after we obtained all these instrument characteristics, we used the feature in FlowJo that is able to calculate the spillover spreading matrix between the different fluorochrome combinations. Stuff like this makes it easy to recognize which fluorochrome combination should best be avoided as they do have a very, very high level of spillover into each other. If it cannot avoid using some of those combinations, you should try and assign the dim receptors to the bright dyes that do have a minimal spillover. In the table, we can see some examples where you thought our PE-Cy5 and a BB700. That value is quite high. If you had two dim populations on either of those fluorochromes, you may not be able to resolve that population very well.

0:24:21 - 0:26:22, Slide 24

So, here we have another table where we’re able to summarize all the spillover. You can see which fluorochromes are going to give you quite high spillover of that fluorochrome into the other detectors. You can actually calculate the total spread from each dye contribute. It kind of let you know whether that dye is going to work well or play well with some of the other dyes, which is very useful when you’re designing a new panel and also when you’re adding one or two extra fluorochromes or markers to an already prepared panel.

Down the bottom here, you can also calculate the total spread. Detectors that have got very low values will be most sensitive for detecting when you’ve got all the other colors used. This is very important when you’re trying to resolve very low dim population. Again, those dim markers try and put them on the brightest dye with the least spread to try and have some – it guarantees success.

0:26:22 - 0:27:51, Slide 25

I’m going to try and explain what I think some of the reasons are for the differences in staining index. Some of the things that may have an effect on the staining index and how well we’re able to resolve our cells of interest is that the laser power and one of our analyzers were able to change the laser power, and we ran some eight-peak rainbow beads just to see the effect of the laser power and the ability to resolve all eight peaks. As we increase the laser power, we’re able to resolve some of the dimmer bead populations. We can see that here we’ve collected two different – we’ve used two different collection filters for these beads and you can see that no matter how high you turn up the laser, some of the dim populations and notice were resolved in the higher wavelength, the 670-30 collection. That’s because the dye emission doesn’t peak around that area. It sort of peaks at the lower wavelength.

0:27:51 - 0:28:40, Slide 26

Here, we also looked at another analyzer where we’re again also able to change the laser power and the effect it had on dissolving eight-peak beads. The laser power is important. Maybe not so when you - for some of the bright expressing beads or bright expressing dyes but where you do have very low antigens or very dim populations, laser power can help improve the resolution.

0:28:40 - 0:29:02, Slide 27

Again, this is another representation of how the laser power does have an effect on dissolving the low bead populations.

0:29:02 - 0:31:14, Slide 28

Optical filters, band pass filters, dichroic filters, they’ve made up different layers in coating that allow you to collect the different wavelengths of interest. Earlier in the presentation I showed a couple of instruments which in theory should have been identical having the same optical layout that we saw that with some of the - we knew that some of the newer instruments gave us a better staining index than the older ones. One reason for this is that the band pass filters in the older instruments have degraded. So, environmental deterioration due to either moisture getting into some of the layers on the band pass filters will have a big impact on how well these filters behave. Brisbane’s in Australia or in the sub-tropics, our summers are hot and very, very humid. No matter how hard we try and control the humidity and take the temperatures at a constant level, there is still a lot of moisture in the air. I’m pretty sure that a lot of our vent band pass filters do degrade just because there is a lot of moisture in the air. Something that you can try out there is, if you take your filters out of your instrument if you don’t think they’re performing well, just put them up against some lot - you can actually start to see some degradation and when you do start to see that, it’s time to swap the filters. There are different vendors that do supply filters that as the quality of the filter goes up, so does the price.

0:31:14 - 0:32:56, Slide 29

In this slide, we can see the different effects of or the effect that filters can have on how well we’re able to resolve populations. Here, we’ve run some eight-peak rainbow beads and swapped only the band pass filter collecting the signal. Everything else was the same, the dichroics, the laser power and we used the same band pass filter of collecting 530/30 wavelengths. Some of the new filters may not be as good as one would think. Some of the older ones, you can see, are so bad that you can’t even resolve some of the higher peaks well. Band pass filters do play a big, big part in how well you’re able to - or how bright a signal you’re getting on your instrument. So, if your instruments are quite a few years old it’s really worth sitting down and checking. Have your filters degraded? Do they need to be replaced? If so, when you do replace them, you’ll find that there is a marked difference on how well your instrument will start to perform.

0:32:56 - 0:35:25, Slide 31

Photodetectors or photomultiplier tubes convert the photon of light being emitted from our fluorochromes and convert these into an electronic signal that we can then measure and display in flow cytometry. As the number of photons hitting the detector increases, the current, the resulting current also increases. Photomultiplier tubes have got different responses to different wavelengths. The manufacturers will select photomultiplier tubes for a range of wavelengths to be able to detect within the visible range. Some photomultiplier tubes have got much better responses to a particular wavelength than others and sometimes they may not be placed in the best, the most ideal spot for a very fit, most suitable wavelength. Years ago, with the instruments I had I used to remove all the photomultiplier tubes from an instrument and then test each one individually for different wavelength responses. I would then place them on my machine in positions that were best suited to collect that particular wavelength. The newer instruments these days make that a little bit more difficult. Everything is sort of bolted down and locked in the instrument. Some of the cell sorters still allow you to do that but most of the analyzers, it’s not so easy to do anymore.

Something else that people should be aware of if you do have very, very bright dyes where you got a very high signal going to that detector, at that higher detection rate, some of the photomultiplier tubes may not respond in a linear fashion anymore, and they do search a way for when the incident light is very, very high.

0:35:25 - 0:36:24, Slide 31

The Q, what is your Q? The Q value gives you an indication of how efficient the detector is able to convert that light signal or that fluorescent signal to an electronic signal or photo electron. You’ve got a cell that has 10 fluorescent molecules being emitted that hit the detector and the conversion is five photo electrons then the Q value would be we’ve got a 50% conversion. The average conversion in most flow cytometers, which is very good is around maybe 20-30%. Not every single fluorescent molecule gets converted into a photo electron.

0:36:24 - 0:37:51, Slide 32

What kind of things affects the Q value? As I showed earlier, laser power but also laser alignment. You may have a laser with really good power but if it’s not aligned properly and it’s not focused properly, you’re not going to get the most optimal signal. You really need to make sure that you look after your instrument. You’ve got to have a clean flow cell or nozzle. Make sure all your stirring, all your objectives are clean, the lenses are clean. If you’ve got mutual density filters in front of some of your detectors, that will also decrease the Q value. As I said before, PMT sensitivity. Some detectors give a better response to some wavelengths and some less sensitive especially in some of the red wavelengths, and also how well does that PMT response to different wavelengths.

0:37:51 - 0:39:18, Slide 33

Linking dye brighteners and fluorochrome response on individual flow cytometers, they are different. These P&Ts have got different responses to different wavelengths. Your cytometer model, how - what it’s optical configuration is. How much noise you actually have in your detector will play a part as well. Do you have good clean power or is your lab reasonably noisy? All these things will have a part. We know that some dyes are brighter than others that because of the variation in instruments, some instrument the collection optics you will get a different response. Also, something that is important is how well you look after your instrument and how clean the optics are. If things do start to degrade, it really is a good idea to replace some of your band pass filters.

0:39:18 - 0:40:38, Slide 34

What can we do to improve how we select different markers associated with fluorochromes when designing our panels? Ranking fluorochromes for instruments, which is what we’ve done is a good start. What we’ve done here on one of our Fortessas is that we’ve grouped the different fluorochromes into very high, high, moderate, low to very low. The actual base groups of high or low fluorochrome brighteners are going to be different for each instrument. The fluorochrome is suddenly going to be very high on one instrument, may not be the same on another instrument. So, people need to be aware of that if they’re going to be staining a certain panel using this design or one of the instruments. They may not get as good a result on some of the other instruments.

0:40:38 - 0:42:40, Slide 35

Things to consider when you are going to be designing multi-color panels, you really need to determine, define how good your dye brightness is on an instrument. Try and avoid any fluorochromes that do spillover. The less overlap there is, the better your resolution. Another suggestion is, if some of the dyes are overlapping then try and use markets for those of overlapping fluorochromes that don’t or express something like T and B cells. Try and choose bright dyes, some of the low expression markers especially the fluorochromes that don’t have a lot of spillover. Understand the biology of your antigen or what you’re trying to – what the question is you’re asking with your experiment. Define the dye brighteners for each analyzer and then also consider the spreading error of that dye on the instrument that you’re going to be using.

Also something you need to take into consideration is some of the markers you’re looking at transient. Do something biological have to happen so that marker should be expressed? In those cases, really try and use fluorochromes that are bright or have very minimal spillover. Know your flow cytometer.

0:42:40 - 0:44:14, Slide 36

What do we need to do? Well we continually update and educate our facility users about dye complexity and panel design. We emphasize the individual optical characteristic within the facility, and we’ll sit down with our users and help tailor their panel to the instrument of their choice. Or we’ve ranked all our dyes and use some of the features and software that will allow you to calculate the spread of your fluorochromes for that particular cytometer, and provide this as a really good resource of facility users to help guide them, prepare the panel design and some of the more complex panels that we’ve been doing for the clinical trials that we do here. We aim to give the best advice and service because we are a service facility to our users and we will modify our instruments as new dyes come on the market doing help to further improve the service to our users.

0:44:14 - 0:44:52, Slide 37

I just wanted to thank the rest of the gang here. It was a pretty major day or days, multiple days when we do this work. Every single person had an instrument and they basically almost spent the whole day on each instrument running all the different meters and all the different fluorochromes just to get all these started together. I guess it’s for everyone and thank you. Any questions?

0:44:52 - 0:45:14 

Thank you, Grace for your presentation. A quick reminder for our audience on how to submit questions. Simply type them into the dropdown box located on the far left of your presentation window labeled “Ask a Question” and click on the “Send” button. Grace Chojnowski will answer as many questions as time permits.

0:45:14 - 0:46:10

First question is: How important is titration of each antibody conjugate used?

It’s very important. As we saw with the staining index, having the highest titer doesn’t always give you the best separation of the cells of interest. You may actually start to increase the background a little bit more if you do have too high a concentration. Then again, if you don’t have enough, if the concentration of your antibody is too low, you’re not going to get a very good signal either. So it is crucial that you do titer your antibody before you start doing any experiment.

0:46:10 - 0:46:26

Thank you. You mentioned doing an antibody titer using comp beads. How does this compare to titration on the cells and what are the advantages and disadvantages of doing titration using comp beads?

0:46:26 - 0:48:00

The advantages are that you don’t need to collect large volumes of blood for all the fluorochromes. You can imagine 61 different antibodies with numerous titers. We wanted to make sure we had the same sample. You do need to collect a lot of blood, which some volunteers are happy to do but after a while – but with comp beads if you get the same lot number, the concentration is the same. The only disadvantage with using comp beads, they do have a lot more receptors on the surface, so the amount of antibody that will bind to them is always a lot higher so you will end up using a lot more antibodies. What we did do, we really try to make sure that the concentration that we used was constant with every comp bead. There’s pluses and minuses to both but comp bead they’re there all the time and there isn’t as much effort in preparing them as what you need to put in when you are using peripheral blood.

0:48:00 - 0:49:43

Thank you. The next one is, I like how you classify dye into brightness categories, say dim, moderate, bright, very bright. What about doing this for antigen density information, say low, medium, high density?

Yes. That is important as well. If you do have a high antigen density, you would rank the way you know what you expect or whether you are going to get a high antigen density and you would use the cells or you would use the fluorochromes that maybe aren’t as bright with some of the antigens that are bright. For example, something like CD-8 has got a very high antigen density you could use some of the dimmer dyes on something like CD-8, whereas, other markers that may have a very low antigen density, you will design some of the brighter dyes. You would also try and use the dyes that are bright and also ones that don’t give you too much of a spillover. The antigens that you’re not sure of, or that you’re testing for, and you don’t know what their expression is going to be, or ones where there is a transient expression, again you would try and assign dyes to those that don’t have too much of a spillover and are reasonably bright as well.

0:49:43 - 0:50:00

Thank you. The next one is, can you summarize the instrument characterization studies you recommend doing for any flow cytometer? Does this characterization ever need to be repeated?

0:50:00 - 0:51:18

It should be repeated every time if you have your photomultiplier tubes changed or the instrument’s realigned, you get a new laser, any of the optical components are changed, it will make a big difference. One of the older instruments that do give us a poor response as we’ve started to purchase a new filters, and we haven’t repeated the studies but once we start doing this again we’d be able to see a marked different and just show the difference between what the characteristics were before we replaced a lot of the filters and then how the instrument behaves after some of the optical components were changed. Realistically, it will - the characteristics will change as soon as you change anything on the instrument. Also, over time as I showed in some of the slides, things will degrade and the characteristics won’t be as good as what they may have been a few years prior.

0:51:18 - 0:51:31

Thank you. Looks like we have time for one more question. That will be this one. What do you think is the main reason for the differences in the staining index on different instruments of the same dye?

0:51:31 - 0:52:41

The main difference could be the laser depending on some of the yellow green dyes. If you’re exciting them with the yellow green laser then you are going to get a better response. Some instruments don’t have a yellow green laser, they’re using a 488 excitation, which isn’t as optimal for those dyes. You’re not actually exciting the dye that’s optimal absorption. The other component which I think is very important are the actual collection filters are found that they make a huge difference as to how good a signal you get depending on the quality of the filter and whether it has degraded. Also, to some effect the actual photomultiplier tube, some just have a little bit better sensitivity just setting spectral wavelengths.

0:52:41 - 0:52:51

I would like to once again thank Grace Chojnowski for her presentation. Grace, do you have any final comments?

0:52:51 - 0:53:09

No, but if any - I can’t think of any at the moment. If anybody would like to ask any questions, please feel free to contact me. My contact details are on our website, the QIMR Berghofer website.

0:53:09 - 0:53:45

Thank you and I’d like to thank the audience for joining us today and for their interesting questions. As Grace said, questions we did not have time for today will be addressed by the speaker via the contact information you provided at the time of registration. We would like to thank our sponsor, Thermo Fisher Scientific, for underwriting today’s educational webcast. This webcast can be viewed on demand through January 2019. LabRoots will alert you via email when it’s available for replay. We encourage you to share that email with your colleagues who may have missed today’s live event. Until next time. Goodbye.

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