1. Individual, intact cell measurements
2. Multiplexed assays per well and cell
3. Morphological phenotyping
4. Artifact rejection and subpopulation isolation

These features and their utility formed the basis for countless novel assays, creating new ways of assessing biology from cell inception to cell death and everything in between.  I am extremely proud to have been a part of it.
There were, however, limitations to High Content Imaging.  The underlying engine of microscopy meant that only cells associated with the bottom of the well, either growing on it like adherent cells or associated with it by settling, centrifuging, or immobilizing in a matrix (like suspension cells) could be focused on accurately enough to acquire the images needed.  For suspension cells which are biologically sensitive to contact with the plate, this caused a great deal of variation, so adherent cells have become the norm in HCI assay formats. Another limitation of the approach lies in the battle between resolution of the objects and the number of objects collected. With imaging, you can’t have both, so you either end up with a compromise to collect a lot of low resolution objects (if your assay does not require higher resolution) or you end up collecting a lot of high resolution images in order to get enough objecst to statistically represent your biology.  Unfortunately, there is a cost associated with storage and analysis of large number of images and many of you have multiple terabyte server farms of images and distributed processing engines whirring away to analyze those images.
But ‘high content’ is still fantastic because of the kinds of data that can be generated and cross correlated in the well and sometimes to the cell.  But not having a high content screening tool optimized for suspension cells always bugged me…and by the number of inquiries about sticking cells down that I would get from the high content community, I guess it bugged you too.
But there is high content technology, as defined by the bullets above, for suspension cells that has been in use for the last 30+ years, and of course I am talking about flow cytometry.  Flow cytometry has all the attributes of a high content approach, but was designed and optimized for suspension cells, beads, microbes, and mixtures of these objects. I have read many articles on our industry over the years and flow cytometry is always mentioned as a great technology, but lacking the capacity and simplicity to be a true screening tool. Imagine my delight when I found out, through the grapevine, that IntelliCyt, headed up by one of my early Cellomics  colleagues, was commercializing technology that would overcome the limitations of current flow cytometry by combining their novel approach to high speed sample loading with a revolutionary, and widely accepted flow cytometer that dramatically reduced complexity, adjustments, and cost.  As I discovered more about the potential and future directions this technology was about to take, it became apparent to me that I had found the missing link— a technology to fill the gap in the high content toolbox.  All the projects to stick cells down, all the times we treated cells like “nails” because we only had a high content “hammer”, could be solved by the introduction of a high content suspension screening platform!  After a lot of soul searching, I decided that this was too compelling of an opportunity to ignore, and I moved by family out to the desert of New Mexico and joined IntelliCyt as the Director of Product Management.
So, this new high content tool needed a name, something short and sweet that linked the technology to the high content family of tools but still retained the essence of what makes it special.  High Capacity Flow (HCF) is the name we chose, because “capacity” is the value we bring to a technique that was high content years before we invented high content. Our vision at IntelliCyt is that HCF and HCI become a complementary set of tools used by the high content community, where suspension screening releases an entire class of cells, beads, microbes, and mixtures from the shackles of compromise.
More to come on the advantages of HCF and where it might be used to optimize your high content workflow….

OK, now I was ready to delve into Derek’s blog about the phenotype.  Derek reviewed an article entitled: How were new medicines discovered?. The paper’s authors, David Swinney and Jason Anthony,  looked back at 259 drugs approved between 1999 and 2008, and noted of the 75 first-in-class drugs, 28 were assigned to phenotypic assays and only 17 to target-based approaches.  Now,  I am having a little problem with the math.  What’s the story with the other 30 first- in-class drugs?  So I googled the original article (results=1) and found out that of the 75 first-in-class drugs, 50 were small molecules and 25 were biologics.  Interesting, and the math works.  The article compares efficiency with effectiveness of the two approaches  and provides some great insights into the high attrition rates and low productivity in pharmaceutical research.

However, I am still intrigued about the evolution of the phenotype. I googled flow cytometry phenotype. (Results = 3,790,000)  Flow cytometry excels at phenotyping.  In my personal experience in the lab, we routinely performed phenotypic analysis of peripheral blood using monoclonal antibodies to identify subpopulations of cells in patient samples to identify or quantify disease states back in the 80’s.  So why has it taken so long for flow cytometry to move from phenotyping to phenotypic screening in drug discovery?  I googled flow cytometry phenotypic screening (Results = 270,000), then googled phenotypic screening (Results= 2,770.000)

Now, I had to know more and found a review article that provides some great insights.  In Multiparameter phenotypic profiling: using cellular effects to characterize small molecule compounds, published in 2009, the authors state that…”cell based screens tend to have higher hit rates than biochemical screens, presumably reflecting the existence of many potential targets” and propose multiparameter  phenotypic profiling as a promising solution for evaluating cellular efficacy and to pinpoint side effects earlier in the drug discovery pipeline.  The authors compare several profiling technologies and listed the advantages and disadvantages of each.  For flow cytometry, the advantages were: 1) Allows single cell response to be measured, 2) High signal to noise ratio and 3) Can be multiplexed.    The disadvantages listed were: Requires many cells and low throughput.   However, they go on to say, “Technology improvements-such as the continuous-flow autosampler” (referencing the paper Flow cytometry for high-throughput, high content screening by Bruce Edwards et. al. describing the HyperCyt)…has enabled high-throughput flow cytometry experiments for profiling purposes.”

I googled HyperCyt (Results = 41,900)  So, after emerging from this rabbit hole, I can see why phenotypic screening by flow hasn’t been more widely used in the past, but now with tools like the HyperCyt or HTFC Screening System, phenotypic screening by flow cytometry is a valid and practical option for researchers in drug discovery and elsewhere to perform multiplexed, suspension cell screening in order to create better drugs faster to treat and cure disease.  Just go with the flow.

Over the next few years, we would run into Joe at increasing numbers of meetings as we grew the company and started attending more conferences. I learned that Joe is a passionate scientist who knew a lot and cared even more about the role and importance of cell-based screening in drug development. Every now and then, it would come up in our internal discussions how we could use a guy like Joe to help us close the gap between flow cytometry and image cytometry by moving flow into screening with suspension assays. I read an article that Joe wrote in 2009, where he said, “The search is on for better tools that can automate discovery processes and yet provide the innovation needed to explore therapies for existing and emerging diseases.” Yep, we thought Joe would fit right in at IntelliCyt.

So, after two weeks working with Joe, I am happy to say that we have a remarkably capable and knowledgeable addition to our team and I think our customers and the scientific community will benefit with the addition of such a champion of cell-based screening to the IntelliCyt team. Go Joe!

As most of you know, we here at IntelliCyt have developed the HTFC Screening System; which is a high throughput screening platform using cell based assays. What is unique about our system is that it uses flow cytometry to measure cellular fluorescence. What you might not know is that our founder, Terry Dunlay, was also the co-inventor of High Content Screening and a co-founder of Cellomics, which pioneered the field of imaging based high content screening. This gives us a unique perspective, having developed both imaging and flow cytometry based screening products.

So back to the question; imaging or flow cytometry? As you might have guessed, I see these platforms as complementary technologies. Let’s look at flow cytometry first. Very simply put, this is a very powerful technology that measures fluorescence signals associated with cells in suspension as they pass through a laser based detection system, one cell at a time. This enables a couple of important features. The first is speed; thousands of cells can be analyzed in one second. This has obvious advantages in terms of statistical robustness. What’s more, multiple colors of fluorescence are detected on each cell simultaneously. This means that it doesn’t take additional time to measure additional parameters. Another valuable feature is that flow is perfect for suspension cell analysis. Suspension cells, including primary cells of the blood and bone marrow system and cell lines representing the immune/inflammatory system have largely been underutilized in high throughput screening environments. With the introduction of high throughput flow cytometry, screening assays employing these important cells are now being incorporated into drug discovery screening campaigns.

Now let’s look at imaging. High content screening platforms utilizing fluorescence microscopy have revolutionized the drug discovery industry. HCS platforms use fluorescence microscopy to capture images of cells in microplates, and then employ sophisticated imaging algorithms to interpret the fluorescence signals present in each cell. Analyzing cells by imaging in microplates thus enables the combination of throughput with spatial measurements. Imaging, then, is great for testing the effects of compounds (or other treatments) on readouts like subcellular translocation of molecules, cell morphology and even cell motility. Because the majority of these platforms work best with immobilized cells, they are perfectly suited for analyzing adherent cells.

This is an exciting time in cell based screening. Each year new platforms are becoming available for high throughput analysis of cells, which gives scientists powerful tools for drug discovery and systems biology. Now scientists have a choice in selecting the most appropriate cells –whether suspension or adherent- in which to perform small molecule or biologics screens, or when performing high throughput systems biology experiments.

First off, with this release we now officially support Windows 7 for both 32-bit and 64-bit systems. Specifically, with Windows 7 64-bit, our software can now use as much memory as you have installed in your computer for data analysis, whereas previously on Windows XP 32-bit, our software was generally limited to 2GB of memory.  So, if your HyperView Analysis computer has 4GB+ or more of memory, and you have been working with large FCS data files and more complex data analysis (i.e. more histograms, populations, statistics, and heat maps), you will see a large speed up in performance in HyperView Data Analysis.

Secondly, we have also re-worked our code to improve our memory usage on all Windows operating systems and we have speeded up our histogram display code.  For users requiring FCS file stitching, you will now find that this process is now two to six times faster than the previous release.

A feature that was requested by IntelliCyt scientists is the Create Logical Wells Population. It is now faster and easier to create well logical populations by simply highlighting the wells of interest using a multi-selection plate control.

Create Logical Wells Population

Other useful new features include:

• Gating on any logical population.  Another request from our internal researchers that they find very useful in assay development.
• Higher resolution for 2D histograms displaying  the time channel.   This is a great troubleshooting tool, for when you want to zoom into a particular area of the histogram.
• Setting compensation is now much easier to use.  Using a floating compensation dialog box, that can be brought up from either Populations or Well Identification mode,  any compensation changes can  be previewed for all the histograms before being committed.
• Plate layout format for exported statistics from stats or heat maps. When you export statistics with the Excel Export button, you have the choice to select a grid format, which is an well ordered list, or plate format.

There are many more features that have been included in this release. If you have not already done so, I would encourage you to upgrade to take advantage of the performance enhancements and great new features in HyperCyt V3.4. Contact us to get the FTP download information.

Virginia Litwin, Ph.D., from Covance moderated the program and began with an Introduction to the Role of Flow Cytometry in Drug Development. Virginia also gave a touching tribute to Phil Marder, who passed away earlier this year and will be missed by all of us who knew him. Phil and Virginia co-edited a book, Flow Cytometry in Drug Discovery and Development that was just published this month. Some of the contributors to the book presented at this meeting, providing additional insight into Biomarkers, Small Molecule Discovery and Oncology Drug Discovery.

The talks were organized to provide some insight into how flow cytometry is being used in the drug discovery process.  Dimitris Skokos, Ph.D., from Regeneron, spoke on New Target Discovery, describing how combining multicolor flow cytometry with high-throughput genomic engineering has allowed them to characterize the full hematopoietic cell profile of genetically engineered mice lacking Delta-like ligand 4.

Addressing Target Validation, Carmen Raventos-Suarez, PhD., from Bristol Myers-Squibb, presented  data which clearly illustrated how, using multicolor flow cytometry, “cells within discrete populations can be identified before and after treatment using cell cycle profiles and molecular markers, thus providing evidence that treatment is within a mechanism and modulating the signaling cascade.” And, on a personal note, I was happy to hear her report that she has found the HyperCyt to be a valuable tool in expediting their research.

Speaking on Small Molecule Discovery, Larry Sklar, Ph.D., from the University of New Mexico Center for Molecular Discovery, presented a number of examples of screening assays using the HyperCyt flow cytometry platform in his talk, “High-throughput Flow Cytometry, Small Molecule Discovery and the NIH Roadmap Molecular Libraries Initiative.” Larry also presented on grant opportunities that are available to researchers who want to collaborate on projects.

Pre-clinical Toxicology was discussed by Dave McFarland, from iCyte in: “The Search for a Biomarker of Drug-induced Vascular Injury.” He also spoke about  flow cytometric analysis of blood and how it  holds the potential to provide such a biomarker.

There were too many outstanding presentations at the Fall Meeting to discuss in this post, but they will all be available soon for you to view on the MetroFlow meeting archive page.

In order to accomplish this, models and tools are required that allow for characterization of critical toxicity pathways such as common signaling pathways (e.g. p53, Nrf2), apoptotic pathways, etc. Much is already known about these common toxicity pathways and can be exploited through the use of commercial kits and published assays. However, in order to build a predictive model, more than just endpoints and assays are required—context needs to be given.

Context can relate to the organ that is being modeled (e.g. liver, kidneys), the whole organism (e.g. human, mouse), or even the compound that is being assessed (e.g. metabolism, interactions). The definition, and ultimately the models and tools, need to be defined up front in order to make successful predictions. Inherently, the difficulty is in both choosing the right systems and also in putting together the information in a way that is meaningful to the original question.

Given that there are many fairly straight-forward fluorescent based assays (e.g. Live/Dead®, Mito-Tracker®) which can be applied to the problem of toxicity testing, there is a lack of integration between these endpoints in assembling a clear picture of cell function and biochemistry.

What is sorely needed is a baseline using relevant cell types that helps to establish the “typical” function of a cell system under a defined set of conditions. Key to establishing this suite of baseline assays is the use of pathway-specific positive controls. Once this is established, perturbations can be made and compared against this baseline.

While many kit assays are available and fairly easy, they are typically used for quick checks of relevant endpoints, or to look at acute effects similar to a maximum tolerated dose (MTD). Many times these single readouts are then used to predict cytotoxic behavior. For example, there is ample evidence that cells that are undergoing necrosis will upregulate many other death pathways and processes, including enzymes such as caspases and release activators like cytochrome C.

With a single-point averaged readout, how is it possible to assemble the correct picture of toxicity?  Bringing together multiple endpoints along key pathways is the next frontier as it relates to cell-based assay work.

Consider the treatment of a single cell line, such as Jurkat cells, with a chemical substance known to be toxic. Even at no concentrations of the chemical there will be cells that are in their death spirals among many healthy and viable cells. In other words, no cell population is completely homogenous. Increasing the incubation time as well as dose will cause more of these cells to enter these death spirals.

However, in every case where some cells exist, there are subpopulations of cells that are completely viable and intact. Any quadrant analysis will easily highlight this distribution. In fact, in this toxicity example, these cells may not have any upregulated stress pathways.

What then is it about these cells that allow them to be non-responders?  Measurements on standard plate readers do not allow for such a detailed understanding of cell population –in short, plate readers provide a total overall or summed response of a population.

Since each cell within a population may respond slightly differently to its environment, an understanding of these differences is crucial to building a new molecular approach. A technique like flow cytometry, with single cell resolution, can provide a better mechanistic understanding of how a compound or chemical affects individual cells.

For too long the industry has focused on isolated biochemical endpoints, ignoring the fact that a so-called cell line is not homogeneous and contains information that may be pertinent to the development of more effective, and safer, drugs.

Let’s take advantage of the fact that we can use whole cell models and powerful techniques like flow cytometry to probe these questions. The promise of analyzing interconnected pathways may be right around the corner.

The idea for the HyperCyt Users Group was initiated a couple of years ago over some beers after a workshop in Boston. Somehow that’s not surprising—great city with a great tradition of ideas and pubs. That was to be, (and now is) the B-HUG. We are making plans for the B-HUG meeting will also be in January.

It was appropriate that one of the inventors of HyperCyt, Bruce Edwards, was there that day. In our workshop at Beth Israel, Bruce introduced HyperCyt technology and described how they were actually performing primary screening of small molecules in the Molecular Libraries Screening Center lab in the University of New Mexico’s Cancer Research Center. Cell and Bead-based screening in 384-well microplates! And fast!

It was (and still is) amazing to realize that they could screen millions of compounds using flow cytometry and in volumes of 15µl and less. The UNM Center for Molecular Discovery published assays cover a range of specimens and targets from bacteria to prostate cancer cell lines, and Quorum sensing to GPCR’s.

So over the past couple of years, more and more researchers are connecting HyperCyts to their flow cytometers or implementing our HTFC Screening System, and discovering more and more applications and challenges for us at IntelliCyt to address. With people running everything from yeast libraries to antibody screening or multiplexed apoptosis assays, we get involved in some pretty interesting stuff!

When we decided to update our website earlier this year, we thought about ways that we could get information out to you, our customers, and get information back that could help us deliver the products that you really need, and a blog makes it easy for us to get information out. So, the HyperBlog for HyperCyt users is up and running. Please subscribe to our RSS feed, and stop by and leave comments to our posts.

There are some great websites in our field that provide a wealth of information. For example, the Purdue Bulletin Board has long been a great resource for general flow cytometry users and recently, Beckman Coulter placed the flow cytometry bible on their website—Howard Shapiro’s Practical Flow Cytometry.

What we propose here is to address the more specific challenges relating to creating robust, reliable screening applications, or simply running suspension samples in microplates using HyperCyt technology.

I look forward to sharing information among all of you brilliant and creative HyperCyt users.