September 18, 2017  |  Announcements, API, Education, Flow Cytometry, viSNE

Don’t Miss Our Next Webinar:
Create & Optimize Your Data Analysis Pipeline

Join us Wednesday, September 27th for our next free webinar. In this field report, you’ll get a walkthrough of how a successful Mass Cytometry core facility applied Cytobank and data analysis best-practices to streamline and grow their operations.


Cytobank’s machine-learning algorithms enable faster, more comprehensive analysis of complex life science datasets for single cell and bulk data resolution. Making the leap from manual analysis to Machine Learning-assisted analysis may seem daunting, but it doesn’t have to be.

We’ll learn from guest presenter Dr. Vinko Tosevski, head of the Mass Cytometry Core Facility at The University of Zurich how he leveraged Cytobank in his operation to build an efficient data analysis pipeline.



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February 23, 2017  |  Education, Flow Cytometry, viSNE

Five Key Takeaways from Our Latest viSNE Webinar

We recently hosted a very popular webinar, “Placing viSNE in Your Toolbox” featuring special guest Dr. Anna Belkina of Boston University School of Medicine. More than 500 investigators registered for our event to learn about cutting-edge tools and techniques for optimizing results from high-dimensional cytometry datasets.

Cytobank screenshots from Bendall Science 2011
Cytobank screenshots on Bendall Science 2011 data

Missed our latest viSNE presentation?
View the webinar recording here:
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June 19, 2015  |  Announcements, Cytobank, Flow Cytometry

Visit us at CYTO and FOCiS!

Come visit the Cytobank team at FOCiS and CYTO next week to learn more about what we’ve been up to, for some hands-on help, to share your wish list or just to chit-chat. We look forward to seeing you there!

Check out the various talks and posters at CYTO featuring Cytobank:

Saturday June 27, 2015

11:00 (Room: Alsh) – The First Multi-center Comparative Study Using a Novel Technology Mass Cytometry Time-of-Flight Mass Spectrometer (CyTOF2) for High-Speed Acquisition of Highly Multi-parametric Single Cell Data: A Status Report A. Nasaar, B. Carter, J. Lannigan, R. Montgomery, N. Paul, M. Poulin, K. Raddassi, A. Rahman and N. Rashidi. Yale Univ. Sch. of Med., Stanford Shared FACS Facilities, Univ. of Virginia Sch. of Med., Dana-Farber Cancer Inst., Fluidigm, Cambridge, MA, Icahn Sch. of Med. at Mount Sinai and Ragon Inst. of MGH, MIT and Harvard.

11:40 (Room: Alsh) – High Content Dissection of Human Melanoma Tumor Heterogeneity during Treatment Using Mass Cytometry J. Irish, D. Doxie, A. Greenplate, K. Diggins, H. Polikowsky, K. Dahlman, J. Sosman and M. Kelley. Vanderbilt Univ. Sch. of Med.

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July 31, 2013  |  Cytobank, Education, Flow Cytometry

Cytometry for Interns

As a summer intern at Cytobank, the past few months have been busy and interesting to say the least. I am currently an undergraduate from the University of Redlands in Southern California. Although I have only been exposed to two years of introductory science courses, I have found that not only has it been easy to adjust to the research environment at the Nolan Lab at Stanford and the collegial atmosphere of Cytobank Inc, it has also been remarkably manageable to learn about flow cytometry.

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June 29, 2013  |  Education, Flow Cytometry

Garry Nolan Interview – Why is this single cell stuff so important?

Garry Nolan was recently interviewed by the science blog Garry discusses the origins of his interest in single cell analysis, his lab’s work with Mass Cytometry, and moving past scientific low hanging fruit. He also discusses how his lab makes their techniques publicly available to the scientific community, and even gives his opinion on radical life extension. You can find the interview here, or you can read our summary below.

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October 30, 2011  |  Cytobank, Flow Cytometry

Cloning Experiments in Cytobank

Something we’ve found useful in analyzing our own data here at Cytobank is the ability to clone an experiment instead of having to download and re-upload files. If a colleague has shared an experiment with you and you don’t want to erase their hard work as you begin your analysis, make a clone! If you simply want to save time performing iterations of your own experiment analysis, make a clone! Experiment clones link back to the original experiments from which they were created on the Experiment Details page, so you’ll always have easy access to the original context. We’ve given you a variety of options for cloning, and you can find them under the “Cloning/Copying” section of the Actions box on the Experiment Details page.



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 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.


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

September 22, 2011  |  Cytobank, Flow Cytometry

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

You may have heard about Fluorescent Cell Barcoding, a flow cytometry technique that allows researchers to answer a larger number of questions with the same amount of antibody, as compared to standard flow cytometry experiments [1,2]. We’ve prepared a few resources to help you learn about, perform, and analyze barcoding experiments.


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.

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