It was bad enough that Uncle Vernon decided that the best time to hold the family reunion in Alabama was mid-July, when the humidity blows past 215 percent and the thermometer never drops below 110°F, but now your camera doesn’t seem to work.

 

You’ve crowded all your sweaty relatives into the shadiest spot you could find, but even that relief isn’t enough to ease the spirits as you fiddle with the shutter button and electronic menu.

 

As you continue to fiddle, however, your irritating cousin connects your current difficulties to a recent incident involving an over-enthusiastic barbecue, and suddenly the camera snaps a photo.

 

As your mom tries to determine who has fainted in the heat, you scan your camera, unsure as to why it suddenly worked. Looking at the image, you see people smiling, presumably at your cousin’s little jab. And then it hits you.

 

The camera has a smile sensor.

 

It wouldn’t take a photo when everyone was miserable. But in the presence of humor, that misery changed its appearance from frown into a smile. However temporarily, the phenotype of your family changed.

 

And the same basic technology that took that photo is also available in the research setting where, in its ultimate expression, only experimental conditions that provide a desired change move forward.

 

 

The ultimate goal of any medical treatment is to change a clinical outcome for a patient, whether it is the shrinkage of a tumor, the amelioration of pain, the clearing of skin blemishes or a multitude of other goals. Historically, these gross morphological and pathological changes were the sole basis of intervention as doctors would give a patient medicine and see if they got better or worse.

 

As our understanding of disease pathology at the organ, cellular and subcellular level improved with the introduction of new technologies, however, our approach to health became much more targeted. The gross, blurry picture of health became much more focused and refined.

 

Whole new avenues of drug development opened up with the advent of the omics technologies, where previously qualitative analyses became much more quantitative, and disease states were described in much more specific terms of gene sequence, metabolite levels and protein expression and modification.

 

Unfortunately, despite some amazing medical successes arising from these reductionist approaches, success has not been a given, and despite many new therapies acting precisely as expected on their targets, unexpected surprises have arisen.

 

“I think everyone got really excited about reductionist approaches and particularly genomics and the idea that one gene would equal one target would equal one small molecule,” opines Merrilyn Datta, chief commercial officer at tissue informatics company Definiens. “The fact is that with complex, multifactorial disease, if there’s any heterogeneity in the target mechanism, you run into problems.”

 

Her solution is to reincorporate some of those early medical principles with the newer methodologies, noting, “At the end of the day, we may not know every mechanism of action, but we need to stop a phenotype [editor’s note: phenotype, in this context, meaning disease].”

 

“I think the scientific interest has always been there to understand the phenotype side,” adds Philip Lee, director of global marketing for cell culture at EMD Millipore. “But from a technology and market perspective, I do tend to agree that there is a realization that you can’t keep going ahead with the pure omics technologies without catching up with some of the less-well-developed fields around cellular phenotypes and how to translate the systems and the information into actual behavior.”

 

Both Datta and Jacob Tesdorpf, director of high- content instruments and applications at PerkinElmer, highlight the importance of cellular context by looking at immuno-oncology (see also our special report, Body, Heal Thyself, in the June 2015 issue of DDNews).

 

“Immuno-oncology is all about context in the tissue,” Datta says. “How many immune cells have made it to what part of the tumor? You can’t do that without looking at the phenotype.”

 

“Inflammatory cells can really work in conflicting ways,” Tesdorpf explains. “They can be either tumor-supporting or tumor-killing. And you really need to understand the balance of these different types of cells quite well to get a good prognosis, and maybe even for therapy.”

 

To analyze the immune cell makeup, the predominant tool has been flow cytometry, he continues.

 

“Everybody knows his CD4, his CD8, whatever surface markers that have been used for many years now to classify different types of immune cells,” he continues. “And that works very nicely if you try to look at these cells in blood. But once you come to a solid tumor, flow cytometry fails, and even if you try to mash up the tumor and isolate the cells from the tumor, you lose the context.”

 

This is where technologies such asPerkinElmer’s Opal platform and its multispectral imaging and analysis software step forward, he says, allowing researchers to multiplex immunology markers in a single assay on a single slide and really get at the picture of how the immune response is distributed in the tumor environment. (These platforms were recently described in detail in a review published by PerkinElmer’s Clifford Hoyt in Methods.)

 

“The cells can compensate in so many different ways that if you’re not really looking at the manifestation of cancer and the phenotype of that cancer, it is really tricky to know if you’re hitting right things,” Datta continues, suggesting Definiens has seen an “explosion” in the number of conversations about tumor microenvironment. “What numbers of these types of cells are in the microenvironment under different conditions? Or what’s the relative area covered by immune cells versus non-immune cells in a microenvironment?”

 

“Once you do it computationally, the way you can manipulate the data and find out what matters is much stronger,” she states. “The human eye could maybe count some of the cells, but we can’t do it in the same way that a computer can just massively quantify all different aspects.”

 

To address these issues, researchers have attempted to combine the best features of both reductionist omics technologies and more contextual cellular, tissue and model-organism approaches.

 

 

As was described at length in last year’s special report on cell biology, titled Life Moves On (July 2014 issue of DDNews), a significant amount of movement into phenotype analysis has come from the cell imaging and high-content screening side of the lab, where advances in microscopy and image analysis have both expanded and enriched cell and tissue analysis.

 

This, in fact, is the sole mission of Definiens, which recently hosted an international symposium dedicated to tissue analysis in a process they proprietarily call tissue phenomics.

 

“With tissue phenomics, we ‘datafy’ our tissue with image analysis,” the University of St. Andrews’ Peter Caie told the symposium. “And image analysis…can look at morphometry, can look at biomarker distribution, it can look at many different things. And we build up a complex hierarchical image-based signature, which we use to stratify patients in either a prognostic or predictive manner.”

 

“We really want to move away from probabilities or population probability statistics and move toward personalized, informed clinical decision-making,” he added.

 

And key to this datafication, says Datta, is the ability to correlate that information with other data types such as clinical outcomes or genomics.

 

Caie’s research is a perfect case in point; he used image analysis to correlate morphological features with survival in patients with mid-stage colorectal cancer, in the hope of distinguishing those patients who would respond well to surgical resection from those who would relapse and die.

 

As he described in the Journal of Translational Medicine, his group identified three factors that seemed to correlate with poor prognoses: tumor budding and lymphatic vessel density and invasion.

 

“The ability to quantify prognostically relevant histopathological features, in a robust and routine manner through automated image analysis, will not only standardize the practice and negate observer variability but will free up a pathologist’s valuable time,” the study authors wrote. “We believe that as digital pathology becomes more commonplace within the clinic, automated quantification of histopathological features, as demonstrated here, will become an invaluable tool in the pathologist’s repertoire to stratify high-risk cancer patients.”

 

“The reality is that having the ability of multiplexing gives you the ability to control for certain effects,” offers Tesdorpf. “It gives you a much broader understanding of what is going on in your sample. And ultimately this will provide more robust data.”

 

And as with Caie’s patient-stratification work, some researchers are investing in personalized therapeutic screening, according to Tesdorpf.

 

“They take tumor biopsies and expand the tumor cells from that patient biopsy and then try to basically treat the cells in vitro to find which drugs are most efficient for this tumor at this stage for this patient,” he explains.

 

“There are a lot of companies out there trying to look at either circulating tumor cells, cell-free DNA or DNA from tumor samples to get a good understanding of what genetic rearrangements and modifications are present in the tumor,” he expands on his point. “And then, where there are known drugs that are specifically effective against one of these mutations, direct therapy in that direction.

 

“What I think is also important, especially if you think about individualized screening, is the software has improved even more in providing a number of different tools to really describe the phenotype, first from a features perspective point of view—what kinds of features can you extract and use as descriptors—and then from a clustering, machine-learning point, where you can then use these tools to come up with a robust descriptor and say, if a cell looks like this, that’s a good sign; if a cell looks like this, it’s a bad sign.”

 

 

Of course, cells are not static entities, and there is more to their phenotype than how they appear.

 

“The dynamic aspect of cell behavior is a very important element,” says Lee. “I think we’ve seen a lot of scientific publications around how cells utilize dynamic signaling and various interactions of events to end up at a phenotype.

 

“If you take something as simple as toxicity or cell death, it’s not simply a binary response. There are signals that are changing over time that influence the end result, and the ability to study those dynamics is going to be a very large and growing field for cell biology.”

 

He also offers the example of stem cell differentiation, where the path that a certain cell takes is dependent on multiple events that occur over a long period of time. Thus, he argues, you want a system that can dynamically track how the colony develops and allows you to modify the environment on the fly.

 

“It’s a good illustration of the importance of being able to modify your experimental conditions as the cells themselves are making decisions and changing over time,” he concludes.

 

For Millipore, one of the answers to this challenge came with the coordination of microfluidics with cellular imaging in their CellASIC ONIX platform, which allows researchers to vary experimental conditions over time.

 

“Using our microfluidic control technology with perfusions and solution flow, you can change the different drugs or inhibitors or stimuli that the cells are exposed to while you’re collecting data from them in a live-cell context,” Lee explains. “That is a good first step toward normalizing and giving experimenters control over the cellular environment aspect, which traditionally has been very difficult and causes variation between results in different labs with different experiments.”

 

According to Lee, the company is constantly learning new ways that their platforms can be applied in the laboratory, highlighting the importance of end users in the development of any new technology.

 

“We definitely learned a lot about working with the right types of customers to understand applications of technology,” he says of the CellASIC ONIX platform’s early days. “The only way to get to that level of specialization is to work closely with customers at the leading edge, developing the cells, developing the assays, understanding what matters and what doesn’t matter.”

 

Michelle Visagie and colleagues at the University of Pretoria recently described their efforts to study the apoptotic properties of an antimitotic estradiol in breast cell lines using Roche’s xCELLigence platform. Rather than rely on cellular imaging, xCELLigence uses changes in electrical impedance that occur as cells attach to and proliferate across microelectrodes that run across the bottom of porous multiwell plates, thus allowing researcher to perform label-independent experiments.

 

Publishing in Cell & Bioscience, the researchers showed that the compound blocked cell cycle in three different breast cancer cell lines, and they confirmed apoptotic induction using flow cytometry. Interestingly, despite concentration-dependent inhibition of cell proliferation in all cell types, one cell line was able to recover after 24 hours of exposure to the test compound.

 

Cells, of course, don’t have to move to undergo physiological changes in response to disease progression or therapeutic intervention. In many cases, the changes take place within the cells themselves, whether in the form of altered gene expression or metabolism.

 

This was highlighted recently by the work of Bayer Pharma’s Patrick Steigemann and colleagues, who examined how cells in different parts of a tumor respond to growth in hypoxic or even anoxic conditions. Among the tools they used, which included 3D spheroid cultures and cell imaging, was Seahorse Bioscience’sXF^e extracellular flux analyzer, which monitored oxygen consumption and glycolysis to give a sense of metabolic processing (see sidebar article below, How’d they do that?).

 

At the recent American Association for Cancer Research meeting in Philadelphia, Seahorse introduced a test kit for its next-generation analyzer, the XFp, which facilitates real-time analysis of live cells to determine their baseline and stressed metabolic phenotypes.

 

In announcing the launch, company chief scientific officer Dr. David Ferrick described the system as “a way to map the metabolic phenotype of any cell, regardless of its energy and metabolic status, by integrating our real-time measures of mitochondrial respiration and glycolysis into a single powerful test.”

 

But whether the platform uses imaging or any other analytical process, the end goal is the ability to make informed decisions about the next experiment or the next stage of treatment.

 

 

Even when you manage to identify something that is a potential hit, Tesdorpf cautions, you may not have a clue of what your target might be. And in the past, he says, target deconvolution from that phase was very difficult.

 

“These days, target deconvolution is still a key bottleneck in a true phenotypic drug discovery approach, but at least there are a couple of things you can do,” he enthuses. “There are clustering technologies where you basically use information about drugs that have known targets and may produce similar phenotypes as a starting point to identify potential targets.”

 

“There are siRNA technologies that you could use to pinpoint the target,” he adds, tipping his hat to the omics strategies. “There is a whole lot of genomics information that you have that might help you to find the target.”

 

Part of the challenge, from Datta’s perspective, is helping cell biologists think in computational terms.

 

“Particularly for my generation of graduate students, obviously you learned statistics and you learned to look at data and for structures in your data, but you don’t grow up doing a ton of programming,” she explains. “But I do think the next generation of scientists—we always talk about digital natives these days—have grown up a little bit more with learning computer programming in high school. So I think it’s going to change over time—the line will blur between bioinformatics and cell biology.”

 

Tesdorpf agrees.

 

“The people in our field are getting more and more informatics-savvy,” he says. “There are lots of tools available both from the commercial end as well as from an open-source perspective that help with that.”

 

The challenge as he sees it may be much more in terms of how researchers understand the data they are accessing, as the connection between the feature and the outcome may not be obvious and yet still be highly informative.

 

“It’s easy if you can say a good cell is a big cell and you can measure the size of the cell, which easily translates to a biological effect that is somehow expected or at least is understandable from the type of biology you’re trying to do,” he explains. “But that might not be a sufficient way to discriminate between phenotypes and to make subtle differentiations. What happens with a lot of the better classifiers is that you get a lot of features lumped into one sort of decision-making matrix that are not necessarily easily translated into something that the biologist might relate to.”

 

He offers the example of cellular texture.

 

“Texture is a way to describe an intensity distribution. If I have all of the intensity in my cell concentrated into one single spot, it’s a different kind of texture than if I have the same total amount of fluorescence distributed in some sort of ripples or something like that. Texture is a very robust and convenient way to describe this sort of different distribution, but it is sometimes more difficult for a biologist to accept that a very abstract value like texture gives them a great way of differentiating that.”

 

Datta, however, suggests that this lack of intuitive connection could potentially be a strength of the analytical systems.

 

“My favorite example is from Andy Beck,” she says, referencing the director of the Molecular Epidemiology Research Laboratory at Beth Israel Deaconess Medical Center. “He did a hypothesis-free experiment where he taught the Definiens software about 600 different features—which is a lot; it’s more than people had done prior to this point—and he started doing correlations with different types of data.”

 

“Prior to this, everybody thought any kinds of markers that we find that correlate with longevity in the cancer patient, or let’s say more positive clinical outcomes, are going to be within the tumor,” she continues. “Doing this approach, the computer came out with some stromal markers. Everybody thought there can’t be markers in the stroma, but sure enough, when you let the algorithms run and let the computer mine the data without our bias that it must be in the tumor, you do see these other markers that you wouldn’t have detected before.

 

“It’s the irony that we think we’re more creative than the computer, and maybe we are, but in our biases, we miss things.”

 

Millipore’s Lee, meanwhile, goes back to the heterogeneity issue inherent in cellular systems.

 

“Whether you’re thinking about phenotypes or cell experiments, one of the biggest challenges that cell biologists inherently know in their training is that not every cell is the same,” he presses. “Even if you have a well with 2,000 cells in it and you collect billions of bits of data from it, the fact is that certain cells are doing different things than other cells.

 

“And if you can’t really tease that apart, you’re going to end up with a mass of information that’s really hard to interpret what’s going on.”

 

This is where the lessons learned from omics technologies come into play.

 

 

There is definitely a bit of rivalry between the omics- and phenotype-driven camps, with those in the latter often looking on in awe at what the omics have accomplished.

 

Carlo Bifulco, medical director of the Providence Oregon Regional Laboratory, expressed his frustrations to the tissue phenomics symposium last autumn.

 

“Where are we after 14 years?” he asked. “We’ve made progress, with some very exciting things today. But the progress hasn’t been as fast as I would like it to be.”

 

“I just want to compare this to how things have changed in genomics, and the huge investments getting made in genomics, and how we are a little lagging behind,” he explained, highlighting that between 2001 and 2013, the cost of sequencing an entire genome has dropped from about $100 million to less than $10,000.

 

“I definitely don’t feel like we’ve had that kind of growth on our side.”

 

At the same time, technology developers are quick to acknowledge the groundwork that omics has established in preparing the scientific community for what was to come.

 

“Genomics drove the concept that we could deal with Big Data, we could deal with a whole genome,” offers Datta. “Now we’re kind of importing that mindset over to cell biology.”

 

“Had genomics not driven the whole idea of a bioinformatics pipe and really driven the idea that we’re going to be connected to the Internet and have all kinds of data storage in labs, I don’t know that we would have gotten so far so quickly in terms of our thinking of using bioinformatics for images in big pharma.”

 

No one is arguing that phenotype should replace omics technologies, however, but rather that there is room for the approaches to inform each other. In fact, from Tesdorpf’s perspective, one has limited options without the other.

 

“Even if you are using a phenotypic approach, ultimately you will have a targeted drug,” he explains. “The pharma company discovering that drug will have to identify the target to greatly enhance the chances of getting approval at the FDA.”

 

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One of the key challenges of treating cancer, at least in solid tumors, is less that the cancer cells are proliferating uncontrollably but rather that the tumor itself is quite heterogeneous. Some cells—particularly those near the angiogenic vasculature—may be growing rapidly, while those closer to the tumor core can be more dormant, coping with hypoxic or even anoxic conditions.

 

This may be one reason why cytostatic chemotherapies only provide limited relief from the cancer, according to Bayer Pharma’s Patrick Steigemann and colleagues in a recent paper published in Experimental Cell Research.

 

“Dormant cancer cells could potentially lead to disease relapse after cytostatic-based chemotherapy,” they wrote. “Therefore, targeting this cell population could be of interest to enhance cytostatic-based chemotherapy.”

 

To test this theory, the researchers grew multicellular tumor spheroids (MCTSs) as a model for solid tumors in 384-well clear-bottomed plates and probed their viability against two bioactive molecule libraries using fluorescent dyes and high- content imaging.

 

Initial probing with two known cytostatic agents—cisplatin and paclitaxel—showed that indeed cell death only occurred in the outer spheroid region, reflective of the actively proliferating cancer cells, and that the core cells, once cleared of the dead cells, were still viable.

 

Screening the two libraries (1,120 compounds), the researchers identified nine compounds that selectively killed the inner core of the MCTS while leaving the outer ring of cells viable. Further validating the use of 3D culture, none of these compounds induced cell death in 2D cultures under similar conditions.

 

As two of the nine compounds were well-known inhibitors of the respiratory chain, the researchers examined whether the other compounds giving a similar phenotype impacted cellular respiration. They monitored both oxygen consumption and lactate production using the electro-optical XF^e extracellular flux analyzer, and determined they could classify all of the hits as respiratory chain inhibitors.

 

Repeating the experiments on several cancer cell lines—colorectal adenocarcinoma, epithelial prostate cancer and colon cancer liver-metastases—they noted the same core death phenotype.

 

“Given that cancer cells are predominantly glycolytic (Warburg effect) and the observation that the targeted cells are located in tumor areas with lower oxygen supply, the identification of respiratory chain inhibitors to selectively target cells in MCTS core regions was rather surprising,” the authors wrote, suggesting a level of complexity not previously noted. “However, MCTS slices stained with pimonidazole, a marker for hypoxia, show that the target dormant cells are not hypoxic but rather are located in regions of intermediate oxygen supply, with anoxia only in the innermost spheroid regions.”

 

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TOKYO—As the Tokyo Institute of Technology (Tokyo Tech) notes in a recent news release, in iPS technology, gene overexpression can induce reprogramming of a cell from differentiated state to stem cell state. However, the mechanism of reprogramming via gene-overexpression remains unclear in spite of the reproducibility of iPS technology.

 

However, Daisuke Kiga, Kana Ishimatsu and colleagues at Tokyo Tech and RIKEN Advanced Science Institute now say they have devised a theoretical expression of cell reprogramming, and proved the idea by using synthetic-biology experiments where simplified genetic circuits were constructed in living cells.

 

According to Tokyo Tech, the artificial genetic circuit consists of a bistable basal switch and tunable over-producing system. Modulated induction of over-expression temporarily creates a monostable system “and thus easily controls the inner state of those cells with the circuit. When cells with one of the basal two steady states are modulated, their cell-inner states around the watershed of basal bistable system are affected by the potential landscape of the genetic circuit. In addition to the effect, fluctuation of the bio-reaction divides the cell populations into two.”

 

This cell culture experiment, according to the researchers, demonstrates that the fine and subtle manipulation of the initial cell states, through the regulation of gene- overexpression levels, results in the generation of programmable bimodal distribution from monomodal distribution. The team's mathematical analysis further suggests that the reprogramming strategy can be applied to various types of natural gene networks.

 

The original paper is titled “General applicability of synthetic gene-overexpression for cell-type ratio control via reprogramming” and appeared in ACS Synthetic Biology. The work was supported by the Department of Computational Intelligence and Systems Science and the Earth-Life Science Institute at Tokyo Tech, as well as RIKEN .

 

For more on stem cell issues, see the special report in our August issue of DDNews.

           

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ALBUQUERQUE, N.M. & BASEL, Switzerland—Genedata and IntelliCyt recently announced an alliance that they say “brings together the market-leading strengths of each company to transform screening of suspension cells.” The technology alliance integrates the highly scalable data analytics and workflow capabilities of Genedata Screener software with the high-throughput (HT) flow cytometry capabilities of the IntelliCyt iQue Screener system. The integrated solution is said to enable drug discovery researchers to efficiently screen large libraries against suspension cells and multiplex beads in phenotypic drug discovery, antibody screening, immunology and biomarker research—what the partners say is “an industry first.”

 

With the novel HT technology from IntelliCyt built into the iQue Screener instrument, throughput in flow cytometry experiments is reportedly increased by tenfold to twentyfold over other plate-based flow cytometry approaches. The IntelliCyt iQue Screener system allows analysis of more than 10,000 cells or beads per second, each with six to 15 parameters, resulting in 60,000 to 150,000 data points per second from a given sample.

 

“Advanced computational analysis solutions are required to handle the quantity and complexity of data obtained in high-throughput flow cytometry experiments in large-scale screening applications,” according to Janette Phi, chief business officer at IntelliCyt. “The combined solution of Genedata Screener software and the IntelliCyt iQue Screener platform addresses this unmet industry need. Our solution provides a powerful option for our customers for robust and accurate data analysis across all plates from high-throughput flow cytometry data generated by iQue.”

 

In addition, the Genedata/IntelliCyt solution supports the FCS3 data file standard.

 

“The alliance between Genedata and IntelliCyt underscores our commitment to support all emerging technologies that drive value for our customers,” notes Dr. Othmar Pfannes, CEO of Genedata. “It’s especially exciting to see a screening technology mature and scale to high throughput and that Genedata Screener easily supports yet another technology that empowers researchers to quickly interpret flow cytometry results and optimize result quality.”

 

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SOUTH SAN FRANCISCO, Calif.—Fluidigm Corp. recently announced the availability of Single-Cell ATAC-seq, an epigenetics application for the C1 system that allows researchers to explore the regulatory systems that drive cellular function. This application is freely available to researchers on Script Hub, a new web portal within Fluidigm’s C1 Open App program.

 

As described in a paper published in Nature entitled “Single-cell chromatin accessibility reveals principles of regulatory variation” researchers have used ATAC-seq to identify single-cell DNA accessibility profiles from diverse cell types. Understanding the accessible regions of the genome will reveal the role of DNA-binding proteins, nucleosomes, and chromatin compaction in regulating gene expression. Until now, Fluidigm says, researchers needed at least 500 cells to identify accessible chromatin regions, which misrepresented the heterogeneity present in the biological system.

 

“We believe scATAC-seq will enable the interrogation of the epigenomic landscape of small or rare biological samples allowing for detailed, and potentially de novo, reconstruction of cellular differentiation or disease at the fundamental unit of investigation—the single cell,” according to  Dr. William J. Greenleaf, principal investigator and an assistant professor in the Department of Genetics at Stanford University.

 

Adds Candia L. Brown, Fluidigm’s single-cell genomics business director of product marketing: “We designed the C1 to be very flexible. The Open App program is an ecosystem between method development laboratories and our cell biology users. We created this program to give our users maximum flexibility to expand their capability and experimental strategy over time and to showcase creativity. We’re thrilled to see the C1 community come together and pioneer the next frontier of single-cell biology: single-cell epigenetics.”