This time we interview Harris Fienberg, a Ph.D. candidate in the Nolan Lab at Stanford University. Harris’s most recent publication features his work developing a cell viability detection protocol for mass cytometry using a platinum-based reagent. You can view and analyze the data firsthand via his Cytobank Report.

Send us feedback and let us know who you’d like to hear from (including yourself)!

What are you excited about in science? What is your scientific vision?

Over the last 30 years we’ve gained an extraordinary understanding of the molecular components that make up cellular signaling cascades. However, we’re just beginning to understand how the various components of the cell work together to integrate signals and relay these signals to form phenotypic outcomes. I’m interested in gaining a more holistic understanding of cellular communication. In the future I believe that specific proteins will be seen less in the context of certain signaling pathways and more as words that the cell uses to make a message. I think that reaching this more comprehensive understanding of cellular signaling will be more about about integrating data with smart data analytics than gaining more and more “-omics” style data sets.

What do you study / what is your field?

I use single mass cytometry to study signaling networks relevant to programmed cell death or apoptosis.

What do you use flow cytometry for?

Trying to take over the world.

What are some of your favorite papers?

Fantastic recent paper on using mass cytometry to understand hematopoiesis: Single-cell mass cytometry of differential immune and drug responses across a human hematopoietic continuum. Bendal SC et al. Science. (2011) 332(6030):687-96. Modeling wiz-bang to tell a great story on apopotsis: A systems model of signaling identifies a molecular basis set for cytokine-induced apoptosis. Janes KA et al. Science. (2005) 310(5754):1646-53.

What do you do for fun?

Bike riding, drinking coffee, poultry farming.

What’s your favorite thing about Cytobank?

I love the ability to look at large data sets in parallel. Cytobank has an easy interface for manipulating huge numbers of samples in a straightforward manner.

Interview conducted and presented by Cytobank staff member Angela Landrigan.

Our latest additions to this functionality include the ability for users to change the page size of PDFs such that large illustrations are no longer truncated horizontally.

Here is an overview of the changes:

Page formatting options

You can now select among a range of paper size options for PDFs, including three fixed-size options that constrain the page dimensions if you’re looking to print your plots (Letter, A4, and Poster) and two auto-fit options that scale the width and height of the PDF to exactly fit your document dimensions. The difference between the two auto-fit options has to do with limitations imposed by Adobe Acrobat Reader software, which will only open documents that do not exceed 200 inches in either dimension. So, if you plan on generating very large arrays of plots and want to use Adobe Acrobat Reader to view the PDF, you’ll need to select the “Very Large” auto-fit option, which inserts page breaks every 200 inches. If you do not want page breaks in your PDF and can use alternate software such as Adobe Illustrator to open your PDF, then choose the “Infinite” auto-fit option. With every page formatting option, if you choose a plot size and type combination that exceeds the width of the page format you have selected, an orange warning box will appear on the PDF generation page asking you to select a format with larger dimensions or to alter your plot size.

Name your PDF

After clicking to download a PDF of your saved illustration, you are now redirected to a page where you can specify the name of the PDF that will be generated. This will help you find and organize your local files, and save you the step of having to rename them on your own computer. As always, PDF generation occurs in a separate browser window so you can continue to navigate around Cytobank if you are waiting on a particularly large PDF to be created.

Customizable plot size

The plot size you specify in the Working Illustration is now translated to the print view and PDFs. So, if you choose 128 pixel plots in your working Illustration, your plots will be 128 pixels wide in the print view and PDFs. You can choose plot sizes ranging from 64 pixels to 512 pixels wide.

Gating hierarchy display mimics the view in the Working Illustration

Previously, plots in the gating hierarchy displayed as a vertically stacked line of plots. The gating hierarchy in Print View and PDFs now mirrors the layout seen in the Working Illustration, with plots horizontally stacked by hierarchy and vertically stacked by population, more efficiently using page space when many gates are present.

Header redesign

The overall look of PDFs has changed, with a clean header featuring the Cytobank logo and your experiment’s QR code in the upper corners, and a table of experiment details under the Illustration title.

View our Release Notes for a detailed list of recent PDF improvements. As always, email us if you have suggestions for how new PDF features could improve your experience.

- Angela

For Stanford users, there are two ways to import your data to Cytobank. One is a “Cytobank” link at the bottom of the email users receive after collecting their data.  A user can also initiate data import from the FACS facility by going to http://facs.stanford.edu and clicking on the “Data Archive” link.

Watch our short video to see this in action, or download the PDF guide.

- Angela

This time we interview Joshua Brody, M.D., Director of the Lymphoma Immunotherapy Program at Mount Sinai School of Medicine. Joshua’s recent publications include his studies showing that in situ vaccination with a TLR9 agonist induces systemic anti-lymphoma clinical responses, as well as his studies using immunotransplant to preferrentially expand T-effector cells to cure large lymphoma tumors.

Send us feedback and let us know who you’d like to hear from (including yourself)!

What are you excited about in science? What is your scientific vision?

Joshua Brody, M.D. I’m excited about the fact that FINALLY we are really learning how to use the immune system to make cancers shrink and let patients live longer.  For years, tumor immunologists and ‘immunotherapists’ have thought: ”This should work.  We should be able to make this work!”, but every year our understanding of the complexity of the immune system has become clearer.  Finally, that understanding is being translated into therapies that help patients with cancer.  We have seen it with melanoma and prostate cancer in the past 2 years and we clearly see even more powerful therapies on the near-horizon.

What do you study / what is your field?

Lymphoma immunotherapy.  Occasionally immunotherapy of other tumors.  Very occasionally the signal transduction pathways that lymphoma cells use to grow.

What do you use flow cytometry for?

To impress my grandparents.  They visited the lab once and I told them about the lasers and the droplets and they nodded and seemed pretty impressed.  Also, I use it to assess anti-tumor T cell responses induced by our various vaccine and immunotherapeutic platforms in mice and in people.  We have just begun using flow to ‘dissect’ signaling pathways in patient lymphoma samples after in vitro exposure to novel signaling inhibitors (e.g. PI3Kd, btk, mTOR) and will be correlating these results with clinical results of patients being treated with the same inhibitors.  It should yield biomarkers of which patients are most likely to benefit from which of these targeted therapies and -hopefully- also show us how cancer cells develop resistance to these therapies.

What are some of your favorite papers?

The paper that made me think that lymphoma immunotherapy might work: Idiotype-pulsed dendritic cell vaccination for B-cell lymphoma: clinical and immune responses in 35 patients. Timmerman JM, Czerwinski DK, Davis TA, et al. Blood. (2002) 99(5):1517-26. The paper that made me think that putting T cells into an “empty” person can really kill cancer cells and help patients: Cancer regression and autoimmunity in patients after clonal repopulation with antitumor lymphocytes. Dudley ME, Wunderlich JR, Robbins PF, et al. Science. (2002) 298(5594):850-4. The paper that made me think that the T cells we put into an empty person can be induced by vaccination: Restoration of immunity in lymphopenic individuals with cancer by vaccination and adoptive T-cell transfer. Rapoport AP, Stadtmauer EA, Aqui N, et al. Nat Med. (2005) 11(11):1230-7.

What do you do for fun?

Play with my niece and nephew, snowboard, play guitar, Cytobank.

What’s your favorite thing about Cytobank?

Same as everyone else.  Cytomountains (editor’s note: also know as Heat Plots).  My second favorite thing is that it’s Google-for-my-life.  I can’t remember what experiments I’ve done, and I CERTAINLY can’t remember what the results were.  Thankfully, Cytobank can.

Interview conducted and presented by Cytobank staff member Angela Landrigan.

To summarize what will follow in short: make sure all of your data are on scale, accurately compensated, and make sure all your plots are well-labeled.

Choosing plot types, appropriate statistics, and telling the full story

There are a number of plot types that can help you tell your story in different, visually pleasing ways when used appropriately. Among the flashier ways to display data are heatmaps, histograms, and histogram overlays. These one-dimensional representations owe their appeal largely to their ability to convey an easy-to-understand message: “This population changed in X amount in Y condition.” Where this gets tricky is if you’re trying to describe a heterogeneous population. When deciding on a plot type to use to convey your story, you’ll want to make sure you’re telling the whole story, and not omitting important information about the behavior of subsets in the course of eliminating a dimension of data display. In Cytobank, you can mouse over a heatmap square to display the underlying dot plot, which will reveal another dimension of information of your data.

Figure 1. Example of a well-labeled figure using one- and two-dimensional representations.
Excerpted from Irish JM et al (2010) PNAS, 107(29):12747-54, Figure 1B.
(Click on the image for higher resolution)

Labeling

After you’ve selected plot types and statistical measures, you’ll want to make sure your plots are well labeled in a consistent manner. Adequate labeling is essential to the interpretation of your data. Check that axes are labeled and tickmarks are present for all plots. Make sure that color scales are present for heatmaps and other color-coded representations. Cytobank Illustrations were designed to automatically build plot layouts that contain the necessary labeling components, and elements such as color scale values and the display of tickmark numbers can be customized through the “Plots: Scale Display” box on the left side of the Working Illustration page.

Compensation

In this multiplexing era where multicolor fluorescence-based flow cytometry abounds, the application of a compensation matrix profoundly transforms a dataset. Verifying that data are not over- or under-compensated is another step in the process of making beautiful plots. Consider an example where you’ve stained whole blood with FITC-anti-CD3, PE-anti-CD4, and PE-Cy5-anti-CD8, and have also collected single stain controls. There is overlap among these emission spectra, and so compensation must be applied to discount overlapping signal. You’ll want to determine that the data are appropriately compensated, and not over- or under-compensated.

Figure 2. Assessing compensation using a single stain compensation control layout
(Click on the image for higher resolution)

One way to check for over- or under-compensation is to build an Illustration that lays out plots of your single stain compensation controls, showing data on each channel for each single stain control (Figure 2). You can do this using your single stain compensation controls, which you ran alongside your experiment. With proper compensation applied, you’ll see two peaks in the positive channel and only one peak in all of the other channels. In our example above, the FITC channel will have two peaks for the FITC positive control, and the PE and PE-Cy5 channels will each have one peak when the data are properly compensated. If the data were undercompensated, you would see two peaks in the PE channel. Likewise, if the data were overcompensated, you would also see two peaks in the PE channel. Setting up a layout of your single stain controls viewed across all channels will assist you in optimizing your compensation matrix values. Another way to examine the effects of your compensation is to view all channels plotted against each other in a pairwise plots view for your sample data, which can be found in the Advanced Views section of the Working Illustration on Cytobank.

Scale Settings

Perhaps one of the more important factors to consider when displaying flow cytometry data has to do with scale settings. Zooming in on an example using uniform beads to demonstrate the effects of proper scale settings, Figure 3 shows how to adjust the scale minimum and cofactor using a negative control. In this example, the default Cytobank scale settings were automatically applied to the experiment upon upload (Figure 3A), and the scale settings and cofactor were adjusted for proper data display (Figure 3C). We’ll walk through some background and how to correct the scale display values.

Figure 3. Using negative control beads (eg, comp beads) to adjust scale minimum, maximum, and cofactor.

On a conceptual level, you know that you want all data to be visible in your plots (i.e., no truncation), and that the single cell distributions accurately reflect the presence or absence of populations. A good first step in this direction is to make sure you collect fluorescence data with biexponential scaling enabled in the data acquisition software (for digital machines), as this will allow you to collect data with values below zero. Generally, the collection software will automatically adjust the scale minimum such that all events are captured, and you can always manually adjust these settings.

Moving over to the analysis side, many analysis platforms have a set of standard scale settings that are applied to the visualization of your data, and these scale settings often need to be adjusted to ensure all your data are visible and distributions are properly calibrated. A great way to do this is to build an Illustration that shows channel data for negative controls for each channel (ideally with the compensation matrix you intend to use already applied). This is the same single stain control layout from the “Compensation” section above (Figure 2), and you can built it using single stain compensation bead controls that you probably already ran alongside your samples. You can set experiment scale settings based on these negative controls prior to proceeding with experiment analysis. The above figure shows such a scenario, where the compensation controls were used to adjust scale settings.

Returning to our example in Figure 3, the default scale settings applied to these data included a scale minimum of -200 and a cofactor of 150 (Figure 3A). We can see a spike in the leftmost bin and notice that generally the peak looks flat, indicating that many events are still off scale. So the first step is to lower the scale minimum to ensure all events are on screen (Panel 3B, scale minimum = -1000). Next, we see that signal peak still does not conform to a normal distribution, despite using uniform beads with no signal on this channel.

This is where the cofactor comes into play. The cofactor governs the size of the linear region around zero. The larger the cofactor, the smaller the linear region. By default, the cofactor is set to 150 in Cytobank. In this example, increasing the cofactor to 450 condenses the linear region, resulting in a normally distributed peak (Panel 3C) that mirrors the shape of peaks in other channels. If we lower the cofactor instead of increasing it, the linear region expands and a “data hole” forms near zero, resulting in an artificial display of two populations where we know only one population exists (Figure 3D). By adjusting the scale settings and cofactors for each channel using a uniform negative control, you can ensure that all your data are displayed, that they are revealing populations where appropriate (and not revealing false populations), and have an overall uniform look and feel. The cofactor, scale maximum, and scale minimum need to be set for each channel.

Here’s another example of the effects of scale settings on data display, following the same logic we just discussed. In this example, a 1:1 mixture of stained:unstained compensation beads:

Figure 4. The effects of scale minimum and cofactor on data display: Comparison of scale transformations of 1:1 mix of positive and negative compensation beads (Courtesy of J. Irish)
(Click for higher resolution image)

Be aware that some cytometers default to using a Log10 scale (for example, the FACSCalibur and LSR II), and you have to specifically enable biexponential scaling to collect events below zero. Below we show the pitfall of displaying data near zero on a Log10 scale without biexponential scaling:

Figure 5. Using a log scale without biexponential scaling can result in data truncation.
(Click for higher resolution image)

In Figure 5A, notice that there is a data spike on the low end of the scale near the Y axis, resulting in truncation of data and the creation of an artificial third population. In this scenario, the scale such must be adjusted such that it displays negative events, and using the arcsinh scale allows for configuration of the linear region around zero through adjustment of the cofactor.

Have data display tips to add to this list, or any questions / comments? Leave a note below.

- Angela

This time we interview Ernesto Diaz-Flores, Ph.D., a postdoctoral fellow in Mignon Loh’s lab and previously in Kevin Shannon’s lab, both located at UCSF. Ernesto’s recent publications include his contributions to studies of p53 loss in acute myeloid leukemia, STAT5 activation in myeloid malignancies, and K-Ras(G12D) expression in primary hematopoietic stem/progenitor cells.

Send us feedback and let us know who you’d like to hear from (including yourself)!

What are you excited about in science? What is your scientific vision?

Ernesto Diaz-Flores, Ph.D. Science is a very exciting field because every new question represents a new challenge. As a scientist, my biggest contribution would be to tease out the biochemical mechanisms leading to leukemia and use that information to predict therapeutic responses prior to subjecting the patient to the treatment.

What do you study / what is your field?

One project is to study the mechanism used by oncogenic Kras to mediate myeloid leukemias. My most recent project is to biochemically characterize hypodiploid leukemias. Through the use of a signaling profiling platform, we aim to understand why they do not respond to chemotherapy.

What do you use flow cytometry for?

Most of my approaches use flow cytometry. Flow is very a powerful tool where you can have a very heterogeneous sample and just by applying gates you can analyze biochemical profiles in the subpopulation of interest (i.e, tumor cells, normal cells, stem cells, mature cells…), even if they are all combined and even if one is underrepresented.

What are some of your favorite papers?

Single cell profiling of potentiated phospho-protein networks in cancer cells.  Irish JM et al, Cell (2004). 118(2):217-28. This paper inspired me and showed analytical possibilities that were not possible with conventional techniques. Genomic analysis of the clonal origins of relapsed acute lymphoblastic leukemia. Mullighan CG et al. Science (2008). 322(5906):1377-80. This paper challenged the dogma of leukemic cells of origin. Recurring mutations found by sequencing an acute myeloid leukemia genome. Mardis ER, et al. N Engl J Med (2009). 361(11)-1058-66. This manuscript is the genomic version of what I would envision for protein alteration profiling. And in general, I enjoy reading papers that make important contributions to science while taking the less-traveled road!

What do you do for fun?

Wow, I could go on and on here, but just to mention some of my hobbies, I dance flamenco and salsa, I do SCUBA diving, I do photography (I am currently the president of the Photography Club at UCSF), I go hiking quite often and run at least two times per week.

What’s your favorite thing about Cytobank?

Heatmaps, hands down! I do large scale experiments and heatmaps allow me to show all the results in a very visual matrix. Besides, you can load a large amount of data from your experiment, analyze all the data in very few minutes, and get the results automatically.  In addition, I like that is very easy to change the layouts (Illustrations) and update any changes immediately.

Interview conducted and presented by Cytobank staff member Angela Landrigan.

Quality control issues arise when there is variability in how experiments are carried out at the bench. We will tackle issues relating to data acquisition in a future post. In this post, we’ll discuss analysis-related quality concerns and introduce you to several Cytobank functionalities that are geared towards addressing these.

Where do issues surrounding quality control and analysis consistency arise?

- Multi-center endeavors to collect and analyze data
- Heads of labs who want to maintain consistency in analysis and presentation as scientists flux in and out of the lab
- Companies interested in a unified analysis and presentation style
- All scientists aiming to achieve reproducibility

Replicating data analysis strategies among similar experiments

The trend towards high dimensional flow cytometry, including mass cytometry, allows scientists to assess increasing numbers of targets simultaneously. This means that gating hierarchies will only become more complicated as they evolve to include increasing numbers of cell subsets and signaling information. A question we often receive is how to apply the same gating scheme between similar experiments.

To facilitate consistency between similar experiments on Cytobank, you can import gates from the related experiment into the new one. This ensures that the same gating strategy will be used among related experiments and can reduce error in replicating gating hierarchies. This can also be useful when research groups want to ensure everyone within a group is adhering to a group standard. Once the gates are imported, you have the option of altering gate positioning to tailor it in the event that there are scatter differences from experimental sources of variability.

Gates can be imported from one experiment to another

Centralizing and communicating data from collaborations

Whether you are contributing data to a multi-center clinical trial or collaborating with a team of scientists across the room or across the world, you will likely want to share the results of your research endeavors. Centralizing project data acquired by different researchers, regardless of their physical location, facilitates communication. Linking resulting figures to underlying data, protocols, and other supporting documents ensures that researchers can delve back into the experiment and rework the analysis and interpretation at any time without the loss of valuable information.

The way you share this information, however, can depend on the project and roles of collaborators. Some scientists may want to give full access to their collaborators early on in a research project. Others may want to share the data once gates and annotations are applied but not allow colleagues to alter gates or annotations. Some may want to share only the resulting figures. All want to have the results linked to the underlying data and experiment details.

When sharing results with collaborators on Cytobank, you determine their access permission level: whether they can only view figures, can rearrange figures (but not change annotations or gates), or have full access to the experiment. From an analysis consistency perspective, sharing illustrations on Cytobank allows collaborators to rearrange figures to compare different figure dimensions without having to redo dimensions such as gating, thereby reducing the likelihood of variability among analysis strategies. With a couple simple clicks, a figure can be rearranged to compare by donor, by channel, or by stimulation condition, without having to fully recreate the illustration, saving time and reducing the likelihood of introducing error.

Change plot display with a few simple clicks

After sharing a figure, a colleague may want to see the underlying raw data in order to explore a question that you may not have considered in your original analysis. On Cytobank, all figures are linked to the raw data. If a colleague wants to view dot plots that underlie a heatmap or histogram, the illustration view can be changed with just a couple clicks of the mouse, without having to remake plot layouts and risk mixups. If you have granted access, your collaborator can also adjust gates or clone a copy of the experiment from which the figures originated in order to manipulate the raw data in a new way.

From an organizational standpoint, collaborators may want to group related experiments into projects and have other project members have immediate access to view (and possibly edit) data no matter their physical location. In addition, keeping experiments organized and centralized into projects means that data won’t be lost over time, and well-annotated FCS files linked to analyses will essentially future-proof the data. On Cytobank, experiments that are part of a collaboration can be grouped into Projects, where project members are automatically granted access when an experiment is assigned to that project, no matter the physical location of the project member. Projects enable collaborators to group related experiments, and project leaders can manage project member access permissions, limiting some project members to a view-only mode while granting other members permission to manipulate the raw data themselves. Our Sharing tools enable you to directly share Illustrations linked to raw data with collaborators. By default, you are the only person who has access to an experiment you upload, and our sharing tools give you the power to control who can see your data and how much access they have. In addition to our main server at http://www.cytobank.org, we offer hosted models of Cytobank accessible by only your research group.

Hosted models of Cytobank can help you centralize data from large collaborations no matter where participants are located, and Projects help you set access permissions to figures linked to raw data. Institutions interested in a unified analysis and presentation style would also benefit from centralization.

Preserving data over time

Having well-annotated files is essential to being able to pick up analysis easily where you last left off, no matter how much time has passed. If your FCS files are sitting in a folder on your computer, unlinked to analyses, protocols, and notes, you run the risk of not being able to remember important experiment details when you open that experiment folder a year later, when preparing a manuscript or presentation. We talked about the importance of annotation in a previous post.

The analysis workflow on Cytobank leads you to annotate your FCS files so that information such as gating, stimulation conditions, and time points are associated with your experiment from the start. If you need to refer back to the experiment at a later date, these annotations are preserved and well-organized for easy retrieval. No more having to wonder what parameters corresponded with file Data.001 and risking misremembering. Illustrations you build are linked to the raw data along with any protocols, comments about the experiment (for example, any anomalies), and other attachments in one experiment bundle, so you can always refer back to the raw data and reanalyze as needed. Heads of lab can easily maintain lab continuity as members join and leave the group when data are well annotated and centralized. Cytobank offers tools for PIs to be added to experiments and Projects such that they can supervise work and help preserve this continuity.

Cloning experiments for analysis iterations

Whether you are working with high dimensional flow cytometry or simple experiments, there will be times when you want to explore an alternate analysis strategy without erasing a previous analysis. While you can generate and save multiple Illustration views within one experiment on Cytobank (dot plots, histogram overlays, heatmaps, etc), there may be times you want to perform iterations of an analysis changing how some of your files are grouped within figure dimensions. For example, you might want to try out a different gating scheme while preserving other annotations such as donor, timepoint, and stimulation condition assignments. Or maybe you ran a 30 channel, barcoded experiment and want to break off smaller pieces for analysis. Maybe you are teaching someone to perform flow cytometry analysis and want to start with a blank slate.

To generate a copy of an experiment where every aspect of analysis is preserved except for the one(s) you want to change, use our experiment cloning tools. We have options to carry over information about compensation, gates, annotations, attachments and protocols, reagent names, and more. Read more about this in our recent blog post on cloning.

Send us a note with any questions, comments or suggestions!
- Angela

To help you consider if a hosted solution is right for you, here are some details about these advantages:

Premium Functionality

Having a hosted Cytobank means that you will stay current with cutting edge flow cytometry analysis techniques. Our hosted servers offer premium analysis functionality, such as SPADE and Dose Response Analysis. These tools are not available on the main Cytobank server (www.cytobank.org) and include implementations unique to Cytobank.

Dedicated Computing Resources

All analysis functions, from basic to advanced, are significantly faster on a hosted Cytobank. Because your hosted version of Cytobank will only be accessible to users you designate, the computing resources are likewise dedicated to your group alone. You won’t have to compete for processing power with other users worldwide, unlike on the main Cytobank server (www.cytobank.org).

Access Control and Usage Monitoring

Each hosted Cytobank installation is self-contained and independent from the others — no accounts are shared and no data from one instance are visible in another. Thus, a hosted Cytobank provides an additional layer of security. Only users you designate will be able to login and access resources on your hosted Cytobank. As the owner of a hosted solution, you have the power to appoint administrators from within your group. These administrators regulate server access, choosing whether to require validation of all accounts before they can be used. Usage statistics are available to administrators as well, which can be useful for grant writing and in settings such as a core facility where usage is used to allocate costs.

Premium Support and Quality Assurance

Cytobank maintains secure local and remote backups of each user’s data, whether on our main server or a hosted solution. Likewise, hosted Cytobank servers are monitored 24 hours a day, 7 days a week for outages. All servers are kept up to date with the latest security patches and use the most up-to-date data transfer protocols used by financial institutions. As always, you can be assured that your data are accessible only to you unless you specifically choose to share with another user.

The Cytobank scientific team offers hosted Cytobank users additional training, support, and consulting services for experiment design and analysis.

Contact Us

If your group is interested in a hosted solution, contact us at helpdesk@cytobank.org.

- Angela

This time we interview June Myklebust, Ph.D., a project leader in Erland Smeland’s lab at Oslo University Hospital and former postdoctoral fellow in the Levy Lab at Stanford. June’s recent publications include her contributions to studies on B-cell signaling networks in lymphoma and her work on bone morphogenetic proteins in B cell suppression.

Send us feedback and let us know who you’d like to hear from (including yourself)!

What are you excited about in science? What is your scientific vision?

June Myklebust, Ph.D. Project Leader, Smeland lab Oslo University Hospital New discoveries that change our current biological models or change our view of what can be done in terms of therapeutic options. My scientific goal is to make discoveries from which patients can benefit. One of the challenges in cancer therapy today is to understand the molecular mechanisms for how patients develop fatal drug resistance. In the era of personalized medicine, development of in vitro assays with predictive power for drug-responsiveness, which then can guide the choice of therapy, would also be highly beneficial. I believe the ability to detect tumor cell heterogeneity will be crucial to address these issues, and therefore platforms for large-scale single cell measurements likely will be tiebreakers.

What do you study / what is your field?

I study cell signaling in normal and malignant B lymphocytes with the aim to identify aberrant signaling in the malignant cells.

What do you use flow cytometry for?

A variety of assays! Flow cytometry gives the opportunity to measure many different targets simultaneously, including intracellular phospho-proteins, in a quantitative manner. Because this method gives detection at the single cell level, tumor cell heterogeneity in cancer patient samples can be discovered. Basal as well as activation-induced cell signaling can be measured specifically in the different cell types present within the same sample. The quantitative measurements provide opportunities to compare across different patient samples, and to identify clinically relevant cell signaling features. I am also using flow cytometry for functional readouts in cell biology studies, including identification of cell differentiation, apoptotic cells, tracking of cell division and cell cycle distribution.

What are some of your favorite papers?

The first phospho-flow paper that I read was very impressive, and was why I went to Stanford and Ronald Levy Lab, to learn the technique from one of the pioneers of phospho-flow, Jonathan Irish: Single cell profiling of potentiated phospho-protein networks in cancer cells. Irish JM et al, Cell (2004). 118(2):217-28. The paper by Louis Staudt lab, describing their loss-of-function shRNA screen for molecular targets in cancer, based on retroviral transduction of lymphoma cell lines with shRNAs containing ‘barcodes’ and a corresponding ‘barcode’ cDNA microarrray screen: A loss-of-function RNA interference screen for molecular targets in cancer. Ngo VN et al, Nature (2006). 441(7089):106-10.

What do you do for fun?

After being used to the Californian sun, it has been really hard to adjust back to the dark and cold Norwegian winters, but outdoor activities like cross-country skiing is a good cure.

What’s your favorite thing about Cytobank?

Everything! There are so many features that I find useful in Cytobank. First, that the experiments are organized similar to an ‘e-mail in-box’ gives an excellent overview. The opportunity to define custom scales for data display, and to import an existing compensation matrix into a new experiment is great. That you can share your experiments with collaborators really facilitates international collaboration.

Interview conducted and presented by Cytobank staff member Angela Landrigan.

What are you excited about in science? What is your scientific vision?

Sean Bendall, Ph.D. Postdoctoral fellow, Nolan lab Stanford University The thought of finding new biological paradigms is what gets me most excited.  There are many basic things in life that happen (biologically / biochemically – that is) which we take for granted.  However the underlying mechanism of many of these is completely unknown to us, and thus when they go awry we have little recourse to address them therapeutically.  Currently, in the context of the human genome, we really only have a grasp on about 20% of what’s there.  My overriding goal would be to develop methods / systems to help rapidly fill in this knowledge gap.  Because of the complexity (heterogeneity) of the human system, I believe that single-cell approaches will be best in addressing this and therefore my work will only put an increasing demand on analysis platforms to accommodate it.

What do you study / what is your field?

I study the function (behavior) of and develop methods for analyzing the different hematopoietic and pluripotent stem cell compartments.

What do you use flow cytometry for?

Everything?  When you deal with any sort of real sample that contains a complex mixture of cell-types or cells behaving asynchronously, single cell resolution is a necessary requirement for the analysis.  Flow cytometry is one of the most robust and rapid methods to accomplish this.

What are some of your favorite papers?

The first stem cell paper: A direct measurement of the radiation sensitivity of normal mouse bone marrow cells. Till JE, McCullough EA. Radiation Res. (1961) 14:213–222. Gold-standard phospho-flow: Intracellular phospho-protein staining techniques for flow cytometry: monitoring single cell signaling events. Krutzik PO, Nolan GP. Cytometry A. (2003) 55(2):61-70.

What do you do for fun?

Avoid writing emails.

What’s your favorite thing about Cytobank?

Heatmaps, exporting gated data, custom scaling of scales for data display, ability to “instantly” re-arrange an analysis (illustration) from an existing project.

Interview conducted and presented by Cytobank staff member Angela Landrigan.

Search our Knowledge Base

Our new support site has a Knowledge Base filled with articles to augment your Cytobank experience. You’ll find tutorials on our functionalities using real datasets, articles guiding you in both basic and advanced Cytobank usage, and answers to frequently asked questions. You can browse through article sections or conduct a keyword search of the Knowledge Base.

What kind of support can I receive from Cytobank?

We offer a wide range of support for your flow cytometry endeavors:

• One-on-one WebEx-based training to get you started using Cytobank
• Biology consulting support: How to design experiments, what to measure, selecting reagents, how to build figures, how to share datasets from your peer-reviewed publications
• Troubleshooting
• Data security
• Technical support for networks
• Inquiries about getting started with premium functionalities such as SPADE and dose response

Whether you need assistance using Cytobank or would like scientific input relating to experimental design or analysis, our support staff is here to help and will generally respond within 24 hours.

Visit our Chatroom

Another way to get immediate support is to visit our support chat room, generally open from 10am-6pm PST Monday-Friday. The link our chatroom can be found on the new support page.

Get updates on new functionality, protocols, and conferences

Visit our blog to learn about new Cytobank functionality and content related to flow cytometry experiments. Navigate the links at the top of the blog page to gain access to protocols sheets, handouts, and a list of conferences where you can find members of the Cytobank team. We attend many of the international, national, and regional flow cytometry conferences. You can subscribe to receive blog updates by email and subscribe to our Twitter feed on the right-side column of the blog page.

- Angela

Hosted models of Cytobank

You may be familiar with our main server at http://www.cytobank.org/cytobank/, but did you know that we can host an instance of Cytobank specifically for your lab group? With these hosted solutions, your lab manager or PI controls who has access to your server and can guide the group in configuring projects to keep various research branches well organized. Read our previous post on Projects to learn more about Projects. Hosted models of Cytobank also have the advantage of offering you premium functionality that isn’t available on our main server, such as SPADE and dose response.

Giving your lab manager or PI access by default

When you upload an experiment, you have the option to set the principle investigator (PI) on the experiment, which gives the PI full access to that experiment. In your particular lab, you may want to give more than one person access to every experiment you upload (perhaps your PI and lab manager). To do this, simply create a Project, give your lab manager and PI access to that Project, and then set it to be your “Default Project” under the Profile link at the top of the Cytobank webpage. Now, every experiment you upload will become part of that project, and your PI and lab manager will automatically have access. Giving your PI and lab manager access to your experiments is a great way to promote continuity and data-preservation in your lab.

Backup and keep all your data organized: protocols, presentations, microscopy, and more

Uploading your data to Cytobank, whether for storage, analysis, or sharing, is a great way to create a reliable backup for your data. In addition to backing up your flow cytometry experiments, our servers allow you to backup data of any type, and to associate those data with specific experiments. To backup and associate a protocol, presentation, microscopy image, or any other file type with one of your flow cytometry experiments, simply visit the Experiment Details page for that experiment and use the Attachments or Protocols section to upload additional filetypes. You can also upload experiments consisting of only non-FCS files by creating a new experiment and choosing to upload only those files.

Use our Inbox tools to efficiently manage and rapidly access your experiments

Now that you’ve organized your experiments into projects, attached related non-FCS files, and given your PI access to your data, have a look at some tools we offer to further organize and filter your experiments using the Cytobank Inbox. Read our previous blog post on Organizing Your Experiment Inbox.

Invite a colleague

Are you working on a collaboration, or alongside someone in your lab? Are your PI and lab manager looking to preserve data as people join and leave the lab? Make sure they have signed up for a Cytobank account so that you can easily share your data with them. You can use the “Invite” link at the top of the Cytobank webpage to invite colleagues to join. Now you’ll never have to email FCS files again, and instead can share well-labeled experiments and figures with the click of a button.

- Angela

Related Blog Posts:
Future-Proofing Your Experiments
Customized Sharing Using Projects
Never Email FCS Files Again
Organize Your Experiment Inbox
Cytometry in the Cloud

The Cytobank Story – Read about how the need for dynamic summaries of experiment results linked to the underlying single cell data resulted in the creation of Cytobank.

Cytometry in the Cloud – Advances in flow cytometry now enable researchers and clinicians to simultaneously measure a large number of cellular parameters. Learn about how doing cytometry in the cloud with Cytobank can accelerate data analysis, foster collaboration, and provide data back-ups.

Mass Cytometry: Vaporizing Cells in the Name of Science – Learn about mass cytometry, where antibodies are conjugated to element isotopes instead of fluorphores, increasing the number of cellular parameters that can be assayed in one sample tube. Access the raw data from a Nolan lab dataset published in Science this year, and try your hand at analyzing mass cytometry data yourself!

Future Proofing Your Experiments and Files: The Importance of Annotation – Learn how to annotate your sample files in ways that will accelerate your analysis. From sample collection on the cytometer to annotating files in Cytobank, associating descriptive details with your sample files will ensure your data are preserved for years to come.

Publications Referencing Cytobank – We are seeing publications emerging from around the world that have used Cytobank in their work, including publications in journals such as Science, Cell, Blood, The Journal of Immunology, and several others. Check out our new page listing publications referencing Cytobank and leave us a comment if we have missed any.

Additional Featured Posts:

How to Justify an iPad in Your Grant

5 Terms You Need to Know in Cytobank

Biochemistry at the Single Cell Level (Experiment protocol, video tutorial on analysis, hands-on sample dataset)

Deconvolute, Decode, Decipher! How to Split, Tag, and Analyze Your Barcoded Data on Cytobank

Visit our Newsletter Archive for additional posts.

- Angela

Public experiments and Archived experiments are flagged with "P" or "A," respectively.

The Experiment Inbox is the first page on which you land when logging into Cytobank. By default, you will see “All Experiments,” including public experiments (denoted with a ‘P’), experiments you uploaded, experiments shared with you, and your archived experiments (marked with an ‘A’). You can change your default inbox view on your Profile page (click the Profile link at top of the experiment inbox, and then click “Edit”), for example if you wanted to display only “My Experiments” every time you log in.

Experiment Filters

Experiment filters are an easy way to quickly narrow your inbox contents. If at any time you want to display a certain category of experiments that is different from your current view (for example, “My Experiments” only), simply click on that filter name in the blue “Experiment Filters” box. You can think of experiment filters like folders that contain only certain categories of experiments, and clicking on the filter name pulls up only those experiments. Mouse over the info card (the little “i”) in the upper right of this box to learn what each of the filters displays. (Note that we recently updated how these filters work.)

Inbox search box

Maybe you’re a power user, and even when you narrow your inbox to display only “My Experiments,” the list is still too long to scroll through manually. Well, there are several tools to help you quickly hone in on the experiment you seek! If you happen to remember any part of the experiment name, entering it into the “Search” box at the top of the inbox will further narrow the list of experiments to only those containing that search phrase. If you want to glance through your experiments by alphabetical experiment name, date modified, primary researcher or other such experiment details, just click the column heading and your experiments will be ordered based on the information in that heading (e.g., in order by date last modified). If you don’t tend to remember the details of experiment descriptors, then “Labels” may be for you.

Labels

Labels allow you to tag experiments based on a commonality among them – for example, you could label some experiments as “Human Experiments” and others as “Mouse Experiments.” You might already be familiar with this concept if you use Gmail. When you apply labels to experiments, the new label appears next to the experiment name and in the list of Labels on the left side of the inbox page. Clicking on a label name allows you to narrow down the experiments displayed in the inbox to ones possessing this label. You can also use the search box to search for experiments containing a certain label. You can add labels to experiments at any time, but we suggest using them from the beginning to avoid frustration when hunting for experiments down the road! Learn more about how to create and apply labels on our documentation site.

Projects

If you’re already familiar with Projects, Labels might sound conceptually familiar. That’s because you can also group experiments by creating and assigning experiments to a Project (and then clicking on the Project name to display only those experiments). The difference is that Projects allow you to specify other Cytobank users who can have access to those experiments and to specify their access level permissions. You can read more about this in our previous blog post on Projects.

Archive old experiments

Several users requested the ability to archive experiments they no longer frequently access, to clear the clutter out of their inbox while still ensuring their data would be around for future access. We heard your requests! You can now find an “Archive” button at the top of the inbox. Simply check the checkbox next to the experiments you wish to archive, and then click the “Archive” button. This will eliminate these experiments from the “My Experiments” view, and you can pull them up at any time by clicking the “Archived Experiments” filter.

Top Experiment Variables and Top Channels

Finally, you can use the Top Experiment Variables and Top Channels to display experiments based on experiment-specific parameters. The Top Experiment Variables section groups your experiments by the most commonly used experiment variable tags. In this section, you’ll see a list of your most commonly used Conditions, Timepoints, Doses, Individuals, and Sample Types, and you can click on these parameters to show only those experiments in your inbox. The Top Channels section likewise displays your most commonly used channel annotations. For example, maybe you have 50 experiments using pERK1/2; you can display only those experiments by clicking on the pERK1/2 link. Remember to future-proof your experiments by annotating your samples as you collect data on the flow cytometer! This will help automate the experiment variable tagging and channel labeling process on Cytobank and help you make use of these Inbox tools.

Save yourself time down the road when you need to revisit data or prepare presentations and publications – get a jump start on labeling and organizing your experiments now!

- Angela

CLONE EXPERIMENT

Choosing to clone an experiment makes a full copy of the experiment, complete with all FCS files, gates, annotations, reagent labels, compensation matrices, protocols, and attachments. Let’s suppose a collaborator has shared an experiment with you. You want to tweak the existing gates without having to redraw them entirely, but don’t want to overwrite the collaborator’s own gates. You can clone a full copy of the experiment and then make the changes in your clone, saving yourself the time that would have been spent redrawing gates and the headache of realizing you erased someone else’s hard work. From an organizational standpoint, you may also want to clone a copy of an experiment shared with you if you want a copy that contains only your own saved illustrations, notes, and attachments, including presentations.

With your own experiments, you might also want to make full clones if you want to subtly tweak existing gates or annotations to perform slightly varying analyses of your own data. “Clone Experiment” can help you do just that.

When you clone an experiment, the clone name contains "(Clone)" at the end, by default.

SELECTIVE CLONE

Selective Cloning allows you to choose subsets of FCS files to copy into a new experiment while specifying whether to bring over the gates, compensation matrices, annotations, reagent labels, protocols, and attachments – you can choose to copy over some or all of these components, helping you make copies of experiments that can be analyzed in different ways. Perhaps you want to preserve how files are categorized into Conditions and Timepoints, but draw gates from scratch for an alternative analysis – use Selective Clone to clone all files with all annotation, but no gates. Maybe you want to alter how files are categorized into Conditions and Sample Types, but want to preserve gated populations – use Selective Clone to copy all files and gates, but none of the annotations. Selective Clone can help you perform iterations of experiment analysis without having to start from scratch, whether on your own experiments or experiments shared with you.

You can also use Selective Clone to split off smaller pieces of a large experiment for separate analysis, or to separate files that require different annotation, gating, or compensation.

CLONE FCS FILES

There may be times when you want a completely fresh start, for example if you are using a dataset to teach flow cytometry analysis, or if you are a computational biologist trying to automate analysis. Clone FCS files is also useful if you want to share only the raw data with a colleague without sharing your analyses and other related information. By cloning FCS files only, you are copying the raw data into a new experiment without bringing over any gates, annotations, reagent labels, compensation matrices, protocols, and attachments that are associated with the original experiment.

Let us know if you have any questions about this functionality or any others!

- Angela

Background

How does barcoding work? In the barcoding step, samples treated under different stimulation conditions are labeled with concentrations of dye that increase at a defined interval. The use of this dye to barcode effectively means that one cytometer channel is taken up for this code. The distinctly stimulated and labeled samples are then combined into one tube and stained with antibodies against targets of interest. This single tube is then run on a flow cytometer and data are collected for analysis. The most common approach is to barcode different stimulation conditions; however, barcoding can be applied to any distinct populations, such as patient samples or different time points of a stimulation condition.

(Adapted from Krutzik and Nolan, Nature Methods 2006)

Performing the Experiment and Analysis on Cytobank

We’ve prepared a video tutorial that walks you through how to work with barcoded data on Cytobank (skip to 2:01 to jump straight to Cytobank analysis, if you wish). You can follow along by cloning your own copy of Experiment #8938. In this experiment, human PBMCs were stimulated with four stimuli, and two unstimulated conditions were included, resulting in the need to barcode such that these six sample types can be distinguished when combined into one tube. Two barcoding dyes were used to create a 3 by 2 barcoding matrix. We drew gates to define the barcoded populations, exported the gated data into separate FCS files, and proceeded with analysis as usual in Cytobank. In the exporting step, Cytobank computationally separates the barcoded cells into different files, as if you had never barcoded them to begin with. Then files are easily analyzed in standard flow cytometry packages, including Cytobank. Splitting off the barcoded cells into separate files dramatically reduces the processing power (and time) required to perform analysis in any software package.

For a condensed summary of how to work with barcoded data on Cytobank, see our text-based barcoding tutorial.

Additional Information

Want to learn more about the nuances of barcoding, such as how to choose barcoding dyes? Visit our Barcoding FAQ documentation. As always, you can submit a support ticket to request additional help by email or WebEx.

References

[1] Krutzik PO, Clutter MR, Trejo A, Nolan GP. Fluorescent cell barcoding for multiplex flow cytometry. Curr Protoc Cytom. 2011. Chapter 6:Unit 6.31.

[2] Krutzik PO and Nolan GP. Fluorescent cell barcoding in flow cytometry allows high-throughput drug screening and signaling profiling. Nature Methods. 2006. 3(5):361-8.

- Angela

Background

Hematopoietic Stem Cells (HSCs) give rise to all blood lineages and are capable of self-renewal. Clinically, HSC transplantation is under investigation for the treatment of diseases of the blood and bone marrow, including cancer, where a patient’s blood cells are wiped out and replaced with healthy cells that arise from transplanted donor HSCs. Transplant studies in mice have shown that only a few of these cells are necessary to repopulate the entire hematopoietic system.

Human umbilical cord blood is a rich source of stem cells, including HSCs. However, a variety of other cell types populate cord blood and must be removed from HSC preparations used for transplantation. Multipotent progenitor cells (MPPs) are one such population. Derived from HSCs, MPPs give rise to multiple lineages and are present in significant quantities in cord blood, though they are limited in their capacity for self-renewal. Purification of HSCs can be achieved by staining and running cord blood through a FACS sorter and isolating cells with a Lin-CD34+CD38-CD90+CD45RA- surface signature (as defined by Park, Majeti, and Weissman). MPPs can be quantified or isolated by their Lin-CD34+CD38-CD90-CD45RA- signature.

Sample Data

If you would like to try your hand at analyzing HSC enrichment data on Cytobank, we have made available an HSC dataset provided to us by scientists at BD Biosciences. You can find a tutorial to guide your analysis on our documentation site.

Here are a few Cytobank features that will facilitate your analysis (click on the images to enlarge them):

View Populations

In the gating applet, you can view the gating hierarchy you have drawn by clicking View in the Populations box. Click the arrows next to the populations to reveal their descendents.



Gating Hierarchy

With the HSC and MPP populations selected in the Populations Figure Dimension, maximizing the Gating Hierarchy box in the Working Illustration will lay out the gating hierarchy, which directly corresponds to the FACS sort scheme used to isolate the cells.

Illustrations and Percent in Gate

Build an Illustration to compare pre-sort and post-sort samples, with the goal being to compare HSC purity levels. The Percent in Gate features enable you to display the percentage of cells falling within gates. In the example HSC dataset, we can see that HSCs were enriched from 43.05% pre-sort to 99.55% post-sort.

You can learn more about these and other features by viewing the HSC Isolation from Human Cord Blood analysis tutorial on our documentation site.

Forward Thinking

Cytobank can help with data management and sharing, in addition to data analysis. When you upload to Cytobank, the data are securely backed up in case of a personal computer failure and are available to be shared with colleagues or collaborators. Also, the annotation features in Cytobank enables descriptive information for the experiment to be saved. For example, flow facility operators running sorts for users can upload the data to Cytobank, share the pre-sort and post-sort purities, and draw gates and annotate files for their users.

Do you have questions about this dataset or other tutorials? Email us at helpdesk@cytobank.org.

- Angela

Reference:

Park CY, Majeti R, Weissman IL. In vivo evaluation of human hematopoiesis through xenotransplantation of purified hematopoietic stem cells from umbilical cord blood. Nat Protoc. 2008;3(12):1932-40. PMID: 19180077

Jonathan from Cytobank/Stanford recommends what he calls “future proofing” in order to avoid this problem. He explained this approach during a CYTO 2011 Pre-Congress course in his talk titled “Flood Cytometry: Embracing Single Cell Systems Biology (and coping with large cytometry experiments).” In that talk, he outlined four easy steps that are useful for experiments of all sizes.

When collecting on the cytometer:

1. Tag your FCS files with key experiment details (e.g. “Patient-J01 IL-2 15m”)
2. Label the channels you are measuring (before collecting data)
3. Make sure scales and compensations work (before collecting data)
4. Encode clinical sample IDs (don’t use HIPAA sensitive information)

Click the image to download as a PPT slide

You’ll notice right away that these things are “obvious” – you know that you should be following these steps, but maybe you feel like you don’t really have the time. In fact, you don’t have the time NOT to do these steps! Spending a few minutes doing these things at the start of your experiment will save you hours during analysis and protect your data from oblivion.

The annotation process in Cytobank is designed to take advantage of any information you entered during sample collection. The more information you enter during collection, the easier and faster your analysis in Cytobank becomes (watch this video to see an example of annotating the Conditions figure dimension). Not only do annotations help you build and arrange your plots in Cytobank, they are also there to help you or your collaborator identify which FCS file corresponds with which experimental sample.

The main rule for tagging your FCS files during sample collection is to include the information that makes that sample unique within that particular experiment. You want to avoid maintaining a parallel “key” in your lab notebook that explains what your FCS files are. There is often some pressure to collect your files quickly while on the cytometer and thus, you may end up not filling in a unique tag for each file. The concern is not that you will lose the key (which of course would be really bad), but instead that having to correlate the files with the key creates several issues:

1. It’s boring and slow, which creates a barrier to re-using that data later
2. There may be mistakes in deciphering the key and files if it’s being done by a person, especially if it’s not the person who originally collected the samples
3. You lose the opportunity to search your files in Cytobank according to experimental variables (e.g. “show me all the experiments where I studied Patient-J01” or “where I stimulated with IL-2” or “where I measured p-STAT5”)
4. Neither you nor you collaborators are able tell “at a glance” what the experiment was about

Experiment variables are often things like timepoints, stimulation conditions, dosages, what you measured on each channel, and codes for individual patients. In the example above, the file or sample name is “Patient-J01 IL-2 15m”, which might mean that the cells in that file were from Patient J01 and were treated with IL-2 for 15 minutes as part of the experiment.

We recommend the labeling strategy below. The instructions are tailored to BD’s FACSDiva software but are generally applicable to any other data acquisition software for cytometers.

Specimen (or tube) labels

Label each specimen and tube with text indicating how it is unique within the experiment. You might include one or more of the following:

• Sample type: Samples can include major classifications of the samples tested (e.g., wild-type vs. knock-out, tumor vs. normal, cell lines vs. whole blood, spleen, or lymph node).
• Condition: Conditions can represent stimuli or inhibitors used in the experiment (e.g., IL-2, IL-6, PMA, inhibitor14, drug35), or alternatively, growth mediums or cell state (e.g., fresh cells, frozen cells).
• Timepoint: Timepoints are specified durations used in the experiment. For example, in a phospho-flow experiment, these can represent times that samples were allowed to signal before being fixed (e.g., 30 s, 5 min, 4 h).
• Dosages: Dosage titrations are various concentrations of a stimulus or inhibitor used in the experiment (e.g., 0.1 ng/mL, 10 ng/mL).
• Individual: Individuals can be different donors or animals (e.g., donor3, BALB/c5).

Examples:

• 2min IFNa 003
• s2 lp-j110_panel 8 cd3 cd127 cd25_singlets
• 01 healthy whole blood donor1 IL-2 10 ng-mL 15 min
• Patient-J01_AtDiag_Panel1

Parameter (or channel) labels

Label each channel with the name of the thing you are measuring, which is often an antibody specificity. For phospho-flow experiments, the phospho-protein should be specifically identified, and it can be useful to indicate the amino acid residue whose state you are probing.

In addition, indicate in the channel name the way you are measuring that channel. For fluorescence flow cytometry, this is usually a fluorophore.

Examples:

• CD3-PerCP-Cy5.5
• CD4-PacBlue
• CD10-Gd156
• p-STAT5-Ax488
• p-p53-S15-Ax647
• Foxp3-EGFP

Compensating at the machine

If you compensate at the machine, Cytobank will see a “file-internal compensation” that you can use. If you want, Cytobank can extract this and save it as an editable software compensation, so you can always tweak it later. (Watch our video tutorial on modifying file-internal compensation.)

Codes for clinical samples

For clinical work, we recommend users create a coding system for human patients so that 1) they never put private patient information into flow cytometry files and 2) they have an easy to read identifier for patients that can be used in figures, slides, and papers.

Getting your labels into Cytobank

After exporting your FCS files from your acquisition software (see our blog posts on exporting from Diva and working with Accuri data), the details that you entered will be included in the name of the FCS file or included within the FCS file itself. When these files are uploaded to Cytobank, the specimen/tube names and parameter labels should be imported into Cytobank and can be used to annotate your files. If they are not, please let us know and we can work with you to see if there’s a way to transfer those tags into Cytobank!

Watch these videos to see how to annotate the Channels dimension or how to annotate other dimensions such as Conditions. The interface for Conditions and similar Figure Dimensions has been slightly updated from the one you see in the videos, but functions in a similar manner.

- Stephanie

A Cytobank project enables you to share multiple experiments with the same group of people. A project also allows you to set project-wide permission levels that let you determine whether or not people can make new illustrations and whether or not they can clone the experiments contained within the project.

For example, let’s say Bob and Joe are in the same lab and both studying leukemia. They often collect flow cytometry data that they want to share with each other. In Cytobank, Bob can create a project called “leukemia project” and make himself and Joe managers of that project. When creating a new experiment on Cytobank, Bob can specify that the experiment is part of the “leukemia project”, and Joe will automatically get access to that experiment. Since they are both managers, Bob or Joe can decide to add new members (e.g. Sue) to their project and decide what level of access this new member has.

One important change in the latest version of Cytobank (2.4.3) is that project managers are now granted full access to all experiments in the project. This change is not retroactive – any project updates that occurred before the 2.4.3 release (July 22-25, 2011) will NOT result in managers having full access to experiments in projects, unless they were explicitly granted full access.

Managers, as their name implies, manage the project. They can change project sharing/cloning settings, as well as add/delete other managers, leaders, and members to a project.

Leaders and members can view the experiments in a project (the level of access is set by the managers) and can add new experiments to a project. Leaders are no different from members, aside from the fact that they’re called leaders. An example of a project leader might be your advisor, who doesn’t need/want to manage the project but would like to stay up-to-date on new experiments.

The level of access for project members can be set as follows:

• Enable/disable making of new illustrations. Managers can decide whether to allow members to make their own illustrations with the data or to allow members just to view the saved illustrations.
• Enable/disable cloning of an experiment. Managers can also decide to enable cloning files only, cloning files and illustrations, or to disable cloning entirely.

When thinking about cloning permissions, it’s important to keep in mind that if you grant a member the ability to clone an experiment, that person can make their clone (and thus the data) public.

For more information on projects, read our documentation article titled What are projects? For a step-by-step guide for how to create a project, read the article titled Share experiments using projects or watch the short video clip below.

- Stephanie

If you’ve logged into Cytobank recently, you will have noticed some changes with the Figure Dimension interface. We’re excited about these changes, which we think will make building your plots even faster and easier than before. We briefly discussed these changes in last month’s release notes and elaborate on them in this blog post.

What do you think of the new interface? Please let us know in the comments below or email us at helpdesk@cytobank.org.

One of the first things we improved was the drag and drop motion of the Figure Dimension boxes (such as Channels, Populations, Conditions, etc.). In the new interface, rearranging Figure Dimensions is much smoother. The Dimension boxes now have grip handles to indicate that the box can be dragged and dropped. When you start to drag the box, a gray box with a dotted outline indicates where the box will drop.

We also increased the width of each box and enabled wrap-around on the label names. This is especially useful with long label names (such as Channel names) which would get cut-off in the old interface.

In the new interface, each Dimension box now has two links:

1. “Choose” is a new feature that enables you to more easily select which annotated files you want to display in your Working Illustration.
2. “Setup” is what we used to call “Edit”. (And as before, the Populations Dimension has a link for “Gate”.)

Choose

In the old interface, you would shift-click to select multiple files, something that became unwieldy when there was more than one “page” of items to select. Now, instead of scrolling within the small Dimension box, an expanded interface allows you to easily select and rearrange the labels in each Dimension. Clicking “Choose” brings up a lightbox that displays all the labels for that Dimension. Checkboxes, filter words, and Select All or None options facilitate selecting multiple labels.

Changing the order of the labels within a Figure Dimension will now rearrange the layout of your plots in your Working Illustration. The grip handle on the righthand side of each label facilitates dragging to rearrange the labels in a desired order. (The Layout Placeholders view still works for rearranging layout.)

Setup

Clicking “Setup” is equivalent to clicking “Edit” in the old interface.

For Channels, “Setup” will bring you to the same page as before, where you can assign your FCS files to different staining panels and edit the names of your reagents. For other Figure Dimensions, “Setup” will bring you to the Annotation page, where you can add labels and assign those labels to your FCS files. This page underwent some small changes recently, as detailed in our release notes for Cytobank 2.3.4.1.

The Populations Figure Dimension contains a link for “Gate” instead of “Setup”. Clicking “Gate” in Populations opens the gating applet, where you can draw gates and define populations.

Watch a short video demonstrating the new Figure Dimensions interface.

Please bear with us as we work to update all of our documentation and video tutorials. If you have any questions about how to navigate the new Figure Dimension interface, you can always reach us at helpdesk@cytobank.org.

- The Cytobank Team