Flow Cytometry Laboratory

The Introduction to Flow Cytometry Class is a series of three 90-minute lectures offered monthly.  The class is led by UWCCC Flow Lab Certified Cytometrists and is designed for newcomers and old pros alike.

This class is the required first step of our training program. All investigators that use or plan to use Flow & Image Cytometry and/or Cell Sorting in their research are required to attend this lecture series.

All lectures will be held from 9-10:30am on a Monday, Wednesday, and Friday, unless otherwise noted.

Registration closes the Friday prior to the start of class at 4:30pm.

Lecture Series Registration

Topics Covered

1. How a flow cytometer works
2. The importance of controls
3. Fluorescence and choosing fluorochromes
4. What compensation is all about
5. Sample preparation
6. Experimental Design, Optimization, and Standardization
7. Image Cytometry
8. Cell Sorting
9. Data analysis
10. Common Flow and Image Cytometry applications
11. Flow Lab policies and procedures
12. Other topics of your choice, questions encouraged

Costs

Cancer Center Members:	$83.57
Non-Cancer Center Members:	$115.98
Wm S Middleton VA:	$128.73
External Academic:	$183.96
Industry:	$276.02

View pricing here:

The Flow Lab Data Drive is for temporary storage and transfer of data files generated in the Flow Lab.  You are the owner of your data and are responsible for managing your data in accordance with data storage and retention policies put forth by the granting agency funding your research.

Accessing the UWFLOW Research Drive

Once you have completed the Introduction to Flow Cytometry class, you will have access to your PI’s folder on the Flow Lab data drive. Your login credentials are your UW NetID and associated password. If you have questions about lab folders and permissions, please reach out to Flow Lab staff at uwflow@uwcarbone.wisc.edu.

Connecting to the Flow Lab Data Drive

You can access the UWFLOW drive space from any computer on a UW network (either by connecting on campus via a wired ethernet connection, or by using a campus VPN from elsewhere). 

Connection paths for the UWFLOW Drive:

Apple:                 smb://dom.drive.wisc.edu/UWFLOW
Linux:                 //dom.drive.wisc.edu/UWFLOW
Windows:           \\dom.drive.wisc.edu\UWFLOW

Computer Access

Access requires physical ethernet connection on campus or campus VPN.

FCS Express

The Flow Lab hosts a license for FCS Express Flow Cytometry Data Analysis Software available to anyone with an “@wisc.edu” email account.  The cost of the license is about $234 per registrant.  It’s an email based license, so you may download multiple copies to multiple computers as necessary.  FCS Express license fees are billed annually in October through iLab along with your regular flow lab usage.

• Register for FCS Express License
• FCS Express Webinars
• FCS Express

FlowJo

The Flow Lab hosts a site license for FlowJo Single Cell Data Analysis Software available to anyone with an “@wisc.edu” email account. The cost of the license is about $220 per registration, per year. FlowJo license fees are billed through iLab along with each lab’s regular flow cytometry fees in the spring of each year.

There are two options available on this site license:

1. Individual User License: Software authentication is tied to an individual FlowJo Portal login. The registered user can install FlowJo on up to 4 computers, and log in to one computer at a time to use the software. This allows flexibility to use FlowJo on multiple workstations both on and off campus. It is intended to be used by only one person. Sometimes called a “floating license”.
2. Shared Computer Registration: Software authentication is tied to the hardware address of a single computer. The license is only valid for that one computer, but anyone with a UW email who wants to use that workstation can make a (free) Portal account to log in and analyze data. This option works well for a shared workstation used by multiple lab members.

Activating a FlowJo License Registration:

Please fill out this form to request a seat on the FlowJo Site License.

Deactivating a FlowJo License Registration:

A registration on the site license remains active until the Flow Lab receives a request to deactivate the registration. Frequency of use does not matter, nor does the lab affiliation of the registered user (for an Individual User Registration) or the retirement of the computer (for a Shared Computer Registration). Email the Flow Lab to request deactivation of accounts/computers as needed. Once a seat is deactivated, it will accrue no new charges. When the next round of software billing is completed (spring of each year), the lab will receive one more bill for the fraction of the year that seat was active.

Billing:

The Flow Lab pays FlowJo in advance for all the active registrations (called “seats”) on our site license, and adds additional seats as needed during the year to fill requests from researchers on campus. The more seats we purchase, the less each seat costs.

Each spring, the Flow Lab looks at the seats that were active on the FlowJo site license for any part of the previous year. The year is broken into quarters and costs are pro-rated, so a registration active for only 6 months counts as 0.5 seats that year. The cost for each registration is billed to the registrants’ labs through iLab.

• More About FlowJo
• FlowJo Webinars

Any published research which received funding from the UWCCC Cancer Center Support Grant (CCSG), including the use of UWCCC Shared Resources to generate or analyze data or conduct clinical trials, must reference the UWCCC. See Acknowledging UWCCC in Publications, Posters and Presentations. Please acknowledge “UWCCC Biostatistics Shared Resource” for any work supported by the Flow Lab.  Thank you!

Sample Acknowledgment

“The author(s) thank the University of Wisconsin Carbone Cancer Center Flow Cytometry Laboratory, supported by P30 CA014520, for use of its services.”

The UWCCC Flow Lab manufactures Cryptosporidium and Giardia standards for water quality testing for Waterborne, Inc.

If you are interested in obtaining water standards for testing, contact Waterborne, Inc. to request information and pricing.

Anthony Gitter, PhD – Genetic and Epigenetic Mechanisms (GEM)

NIH NIGMS R01GM135631

• Given sufficient training data, simple machine learning models can be very effective in finding active molecules for new drug targets.
• Using prior screening data from SMSF, we trained a random forest classifier to predict compounds that inhibit the interaction between PriA helicase and the single-stranding binding protein. The model was tested prospectively on two large commercial chemical libraries.
• First, we virtually screened Aldrich Market Select chemical library (7 million molecules). Among the top compounds scored by the model, we purchased and tested 701 diverse candidates. Of these, 48% were active. In comparison, the hit rate in HTS was only 0.13%.
• Next, looking at performance on an ultra-large library, we applied the model to EnamineREAL (1.08 billion molecules). We purchased 68 diverse candidates prioritized by the model and observed activity in 31 compounds (46%), including one with an IC50 of 1.3 uM.

Alnammi M, Liu S, Ericksen SS, Ananiev GE, Voter AF, Guo S, Keck JL, Hoffmann FM, Wildman SA, Gitter A. ChemRxiv, 2021. doi:10.26434/chemrxiv-2021-fg8z9

Liu S, Alnammi M, Ericksen SS, Voter AF, Ananiev GE, Keck JL, Hoffmann FM, Wildman SA, Gitter A. J Chem Inf Model. 2019:59(1):282-293. doi: 10.1021/acs.jcim.8b00363.

Michael A. Newton, PhD – Genetic and Epigenetic Mechanisms (GEM)

NIH NIGMS R01GM135631

• What is the minimal compound testing data required to learn the broader ligand selectivity of some new drug target?
• Given a well-studied target class, we examined strategies for choosing small, information-rich probe sets or “informer sets” that, upon testing, provide key data to enable inference of a ligand selectivity profile for some new member of the target class.
• As a proof-of-concept, we developed and tested selection strategies that enable effective predictions of kinase inhibitor selectivity profiles on new kinase targets using probe sets of only 16 molecules.
• Excellent performance was observed both in retrospective tests on hundreds of human protein kinases and in prospective tests on 2 novel protein kinases from microbial pathogens.

Zhang H, Ericksen SS, Lee C-p, Ananiev GE, Wlodarchak N, Yu P, Mitchell JC, Gitter A, Wright SJ, Hoffmann FM, Wildman SA, Newton MA. PLOS Comp Biol. 2019. doi: 10.1371/journal.pcbi.1006813

Yu P, Ericksen SS, Gitter A, Newton MA. Biometrics 2022;1-3. doi: 10.1111/biom.13637

John Denu, PhD – Genetic and Epigenetic Mechanisms (GEM)

NIH R01 GM059785

• Overexpressed UHRF1 has been observed in numerous cancers
• HTS of 100K compounds for inhibitors of UHRF1
• AlphaScreen in 1536 well plates
• Three lead compounds identified

Wallace H Liu, Robert E Miner 3rd,  Brittany N Albaugh, Gene E Ananiev, Scott A Wildman, John M Denu. Biochemistry 2022 Mar 1;61(5):354-366. doi: 10.1021/acs.biochem.1c00698. Epub 2022 Feb 10

Tim Bugni, PhD – Developmental Therapeutics Program (DT)

NIH R01 GM104192

• In established cancers, TGF-β is a central player in tumor growth, invasion, and subsequent metastasis.
• HTS of marine natural products for inhibitors of TGF-β.
• Five pyrimidinedione derivatives, biemamides A–E identified as inhibitors
• Biemamides blocked TGF-β-induced transition of the NMuMG epithelial cells into mesenchymal cells.
• Biemamides influence in vivo developmental processes related to body size regulation of Caenorhabditis elegans.

Zhang F, Braun DR, Ananiev GE, Hoffmann FM, Tsai IW, Rajski SR, Bugni TS. Org Lett. 2018 Sep 21;20(18):5529-5532. doi: 10.1021/acs.orglett.8b01871. Epub 2018 Aug 30.

David Andes, MD and Tim Bugni, PhD – Infectious Diseases

U19 AI109673 and U19 AI142720

• A novel anti fungal agent Turbinmicin discovery from marine microbiome using MDR drug discovery platform.
• Turbinmicin displays potent in vitro and in vivo efficacy against multiple MDR fungal pathogens.
• MOA hypothesis is that turbinmicin impairs vesicle-mediated trafficking by inhibiting Sec14p.

Zhang F, Zhao M, Braun DR, Ericksen SS, Piotrowski JS, Nelson J, Peng J, Ananiev GE, Chanana S, Barns K, Fossen J, Sanchez H, Chevrette MG, Guzei IA, Zhao C, Guo L, Tang W, Currie CR, Rajski SR, Audhya A, Andes DR, Bugni TS. Science. 2020 Nov 20;370(6519):974-978. doi: 10.1126/science.abd6919.

Christian Capitini, MD – Developmental Therapeutics Program (DT)

• Management for high-risk neuroblastoma (NBL) has included autologous hematopoietic stem cell transplant (HSCT) and anti-GD2 immunotherapy, but survival remains around 50%.
• Allogeneic HSCT alone was insufficient to control NXS2 tumor growth, but the addition of hu14.18-IL2 controlled tumor growth and improved survival.

Bates PD, Rakhmilevich AL, Cho MM, Bouchlaka MN, Rao SL, Hales JM, Orentas RJ, Fry TJ, Gilles SD, Sondel PM, Capitini CM. Front Immunol. 2021 Aug 19;12:668307. doi: 10.3389/fimmu.2021.668307. eCollection 2021.