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Spring 2023 Content

FC High Dimensional Analysis 

Our Flow Cytometry Resource Offers High-Dimensional Analysis

by Jim Java

Flow cytometry experiments often conclude with the production of FCS computer files containing investigators' raw results. CART's Flow Cytometry Resource (FCR) can now provide standardized, reproducible analyses of FCS files from flow experiments.

After a flow cytometry experiment, it's not uncommon for investigators to import their FCS files into software such as FlowJo or FCS Express, and then proceed with an analysis "by hand", which can be time-consuming and somewhat subjective. In the interest of saving time and limiting subjectiveness, the FCR data analysis team has developed a soup-to-nuts analysis pipeline for the R programming environment: we call it "flowpipe" and it can semi-automatically handle most analytical tasks from pre-processing/pre-gating to phenotype clustering to differential-expression modeling.

The results of a flowpipe run include UMAP visualizations, spreadsheets summarizing the phenotype clusters, per-cluster FCS files, and a detailed summary report of sample or group differences. Although we developed flowpipe to be useable by researchers savvy of R programming (and we're glad to help you set it up!), we recommend that you request a flowpipe run as part of your FCR scheduling, so that our data-analysis team can manage the process.

The length of a flowpipe analysis depends on the number and size of the FCS files provided to the software, but a typical run takes a few hours. Our software has aggregated a number of common techniques and algorithms (well-represented in the peer-reviewed literature) into a flexible parallel-processing framework that's meant to reduce investigators' analytical workload; so, contact us if you'd like more information about sending your FCS files through the flowpipe pipeline.

As part of the flowpipe analysis process, we ask investigators to provide "metadata" relevant to their flow-cytometry experiment: that is, for example, whether samples are cases or controls; a list of pre-defined phenotype gates for drilling down to interesting cell subsets; and patient data that can be incorporated into the differential-expression models. For more information, please check out the flowpipe GitHub respository or contact Jim Java. We can also provide statistical analyses outside the scope of the pipeline—inquiries are welcome!

Proteomic Data Acquisition

Proteomic Table 1

Our Mass Spectrometry Resource Laboratory Overhauls Proteomic Data Acquisition

by Kyle Swovick

Over the past two years, the MSRL has been undergoing an overhaul of our proteomic data acquisition methods. 

For decades, proteomic data has been primarily collected through data-dependent acquisition (DDA). In this method, the mass spectrometer isolates and fragments just a single peptide for identification and then repeats this process throughout the entire gradient. Recently, a method termed data independent acquisition (DIA) has been introduced that promises vast improvements over DDA proteomics. 

When performing DIA experiments, the mass spectrometer isolates and fragments every peptide within a predefined mass range. Performing fragmentation and identification this way, in theory, offers several benefits including greater coverage and less missing values. These gains are primarily a result of the stochastic nature of peptide fragmentation in DIA. DDA proteomic experiments historically have been plagued by high intensity peptides since those will have a greater propensity to be chosen for fragmentation, thereby ignoring many lower intensity peptides. 

DIA experiments however alleviate this problem; regardless of a peptide’s intensity it will be fragmented, thus leading to more possible fragment ions that can be used for identification. When the instrumentation improvements offered by DIA are paired with the recent advancements of neural network and machine learning programming, the results are truly extraordinary. 

Using cutting-edge techniques, the MSRL saw nearly 100% and 50% increase in tissue and cell culture samples respectively (Figure 1A). Their DIA pipeline also saw a 25% improvement in data completeness meaning (Figure 1B). 

Combined, these improvements mean users can result in up to a 150% improvement in their calculations when measuring differentially expressed proteins. If you are curious about what kind of coverage DIA can yield your specific biological matrix, Table 1 includes many of the common sample types the MSRL handles. And, if you are intrigued by what DIA can offer for your own research, you can reach out to MSRL with any questions. 

Stay tuned for the next installment where the MSRL will talk about the improvements they’ve made to their data analysis pipeline to help researchers delve into and interact with their data.

Beer in the Sheath Tank

Experiments in the Kitchen: Beer in the Sheath Tank

by Steven Polter

Whether or not some of us want to admit it, we all still play pretend in some way or other. I, myself, enjoy to pretend I am a brewmaster. I’ve been a homebrewer for years and many of my associates, including my brewing partner, will tell you that my relationship with brewing flirts with the line between hobby and habit. In 2022 I had the pleasure of being asked to play brewmaster for CART and provide a few kegs of beer for a retreat last July. Recently I have been asked to don the mask once again to share and discuss one of those recipes, so I chose to share Supercrisp 570, an American Kolsch.

Perfect for the spring days just ahead of us, this beer beckons the reawakening from winter’s dim but cozy torpor. Bright straw yellow and exuberant, Supercrisp 570 positively pops with a floral nose and lemon-citrus flavor backed by pleasant, bready malt. This brew was designed to shine no matter when or where you drink it! 

Ingredients (For a 5-gallon batch)

  • 12 lb. 2-Row Brewer’s Malt (milled)
  • 3 oz. Lemondrop Hops (T-95 pellets, 2 oz. for the boil and 1 oz. for dry hop)
  • 1 pouch WLP 810 San Francisco Lager Yeast
  • Water (~7.5 gal. total)

Brewing notes

  • Set yeast and hops aside to come to room temperature during the process
    • Heat H2O to 135F* and add milled malt. Stir to mix well. Rest 20 minutes at 125 F
    • Raise temp and rest 30 minutes at 140 F
    • Raise temp and rest 30 minutes at 150 F
  • Mash out and set sweet wort aside
  • Sparge with 3.5 gal. H2O at 170 F for 10 minutes. Recirculate/vorlauf until the wort runs clear after the 10 minute rest, then sparge out into your kettle containing the sweet wort from the first run
  • Boil 60 minutes. Add hops as follows:
    • 1 oz Lemondrop 60 minutes (this notation means the hops spend the listed amount of time in the boiling beer, in this case these hops are added just after the wort begins to boil)
    • 1 oz Lemondrop 30 minutes
  • When boil is complete, cool wort to ~70 F
  • With clean hands and using a clean, sanitized funnel, transfer the wort to a clean and sanitized fermentation vessel. Take a sample of wort at this point for testing of specific gravity. Place a foil cap over the mouth of the vessel after the wort is transferred while the yeast is readied for pitching
  • Again with clean hands and using clean/sanitized scissors, cut the yeast pouch carefully over the open mouth of the fermentation vessel and gently, carefully pitch the yeast into the wort
  • Replace the foil cap over the mouth of the vessel and CAREFULLY shake the vessel vigorously for 30 seconds to 1 minute (this serves to oxygenate the wort which is crucial for initial yeast health/activity as well as mix things up nicely)
  • Ready a clean, sanitized airlock and stopper assembly and quickly peel back the foil cap and place the stopper/airlock combo firmly into the mouth of the vessel
  • Label your vessel (I speak from experience) with the name, date, and original gravity of the wort
  • Give your vessel a gentle slap on the side and take a moment to feel accomplished, maybe crack a beer
  • Consider covering your fermentation vessel with an old t-shirt or whatever else will help keep light out of it. Seriously, being a fungus yeast is not in love with bright light or direct sunlight. Definitely not bright, direct sunlight, which will also zap your tasty, hard-earned flavor compounds!

A step mash is a technique that entails resting the mash at increasing temperature steps to maximize sugar extraction and provide a greater breadth of sugar types in the wort as well as leaving some non-fermentable sugar, which provides a pleasant, bready sweetness in the finished beer. When heating the water for the first step in the mash, be sure to overshoot by about 10 degrees F, as the thermal mass of the grains when added to the water (in these relative volumes) will sink about 10 degrees off the temperature of the mash after mixing.  


  • Ferment at ~60 F (basement/cellar temperature is prime for this!) for 3 weeks
  • Transfer to secondary fermentation and add 1 oz Lemondrop. Secondary for 19 to 21 days, still at ~60 F
  • Transfer to keg and pressurize to begin carbonation. If possible, place the keg into a temperature controlled lagering chamber and step the temperature down by 2-3 degrees F each day until it reaches 35-36 F. During this process, pressurize the keg each day (to somewhere around 25 PSI) to slowly carbonate the beer while you cold-condition it. If there is no access to a temperature controlled lagering chamber the keg can be pressurized and cold-conditioned in a regular old refrigerator or kegerator without temperature control. The beer will still be good!
  • Cold-condition and carbonate in this way until desired carbonation level is reached. Cold-conditioning can be continued after carbonation level is reached, up to 3-4 weeks. Periodically draw small amounts of beer from the keg to pull out any sediment that has crashed to the bottom of the keg, and to taste, of course!
  • Serve and enjoy!

If you are not experienced in brewing and have question marks for any reason, feel free to reach out to me at Steven Polter and I will happily discuss, clarify, and provide additional information!