Who invented dna microarrays

Spotted microarrays, it is argued, are more flexible and more easily adaptable to a variety of research problems in genomics. Also to the point, spotted microarrays are inexpensive by comparison to Affymetrix chips [ 31 ].

Brown in fact has been so committed to the low cost production of microarrayers and an open source approach as a means to expedite the production of knowledge in genomics that he posted on his Stanford website all the details of manufacture for his microarray system, including all the software updates for operation of the scanning system, details on manufacturing and servicing the printing tips, and other fine points of the system.

To study the adoption of both in situ and spotted microarray technologies, we considered the first academic articles either reporting studies based on using DNA chips or simply discussing DNA microarrays. We focused on pre studies because the DNA chip-based research began to take off in These articles broke down into four main types: results of microarray studies, overviews of how to use gene chips, technology forecasts, and descriptions of new or otherwise improved DNA chips.

Of the early articles we studied, by far the majority reported on the results of experiments using DNA chips. Most of these studies aimed to uncover significant genetic information in areas of existing interest, such as cancer and cardiovascular disease; to understand the role of genes already identified as being important to particular diseases; or to attempt wide-scale gene expression monitoring of organisms whose genomes were already heavily studied, such as the Arabidopsis plants and Saccharomyces yeasts.

By addressing several different research communities, these initial studies served to broadcast the potential of the new microarray technology. While these studies were excellent advertisements for the technology, they also opened up promising avenues of inquiry, helping the technology establish itself in a variety of research areas.

Getting involved with the technology early on was not simply a matter of desire; generally, early authors had some affiliation with Affymetrix. In part because of their longstanding relationships and ongoing collaborations with Affymetrix and due to their internal microarray development efforts largely arising from their collaboration on the Human Genome Project , Stanford and the NIH also possessed a great deal of in-house expertise in using microarrays, which allowed them to assist researchers from other organizations in using the technology.

In fact, when we tabulated the affiliations of scientists appearing on the pre microarray studies, we found that Stanford, NIH, and Affymetrix appeared most often. Perhaps unsurprisingly, the first organizations to publish studies based on research using DNA chips were often those that had the strongest links to gene chip manufacturers.

In fact, the second most common organization to appear as an author affiliation among the studies we surveyed published prior to was Affymetrix. The NIH's massive network of intramural research and its strong links including research collaborations to Stanford and Affymetrix made it third.

It was not simply a matter of being involved with the development of various microarray systems that led to publication, later research collaborations with and between these expert organizations also coincided with earlier and more frequent publication. The top three organizations listed above — Stanford, Affymetrix, and the NIH — were the major "hubs" or highly connected points in co-authorship networks for the studies we surveyed. To study the network of collaborations during this early phase of research using gene chips, we used an analysis tool that graphically places organizations according to their co-authorships with other organizations [Note N].

Furthermore, although Affymetrix and NIH co-authored papers together, they also co-authored papers with several organizations that did not co-author with both organizations, thus Affymetrix and NIH are pulled some distance apart as opposed to NIH and Stanford, the large red node, which share more institutional co-authors [Note O]. There were many organizations represented in the first articles dealing with DNA microarrays, but Stanford, Affymetrix, and the NIH emerge as major nodes in this network.

Often, other organizations would partner with one or more of these major players and then go on to collaborate with organizations previously outside the network. The heavy overlap of collaborations indicates that this was a fairly tight-knit research community. Several organizations in the center and upper right of the map collaborated with at least two of the three major players. Interestingly, many of the initial participants in microarray based research were also involved in the Human Genome Project.

Institutions that were the first to publish microarray studies and that collaborated with DNA microarray makers were also the best able to attract federal funding for microarray based research [Note P]. This particular phenomenon in the academic setting of being first to collaborate with Affymetrix, subsequently being first to publish DNA microarray based studies, and in turn receiving more federal funding is roughly analogous to the type of positive feedback loop that economists have used to describe how initially successful high technology firms become increasingly entrenched within their industries.

DNA microarray makers such as Affymetrix have been hubs for an expanding network of companies and technologies across the spectrum of technologies fueling contemporary biotech, gene-based medical therapies, and areas of materials science. These companies have drawn heavily upon academic researchers as consultants and scientific advisory board members, and they have collaborated with academic researchers in sponsoring postdoctoral work and a variety of research projects funded by the NIH, NSF, DOE, and other federal agencies.

The academic researchers involved have only in rare cases relinquished their university positions to move into industry. While some of these individuals, such as Schultz, Berg, Stryer, Ron Davis, Mark Davis, and others have been involved in numerous startups, they have returned to their universities Stanford and UC Berkeley where they have continued to develop graduate programs that incorporate these new innovations. Other Stanford faculty, such as Fabian Pease and Calvin Quate, have continued as advisors and collaborators in shaping new generations of microarray and sequencing technologies at Affymetrix.

Through these technologies and the academic researchers who have participated in developing them, research programs at Stanford and other universities in a variety of different disciplines have taken new shape and direction. For the totals from nearly all fields citing microarray research, see Appendix A [Note R]. The data show that interest in DNA chips and microarrays more generally was manifest in a variety of disciplines.

As a new, promising, but unstable and unproven technology, microarrays were attractive as a platform that could be improved upon by many different fields. In an era when researchers were motivated to find new ways to interpret the massive amounts of data being generated by the Human Genome Initiative, researchers in just about every field of biomedicine were looking for novel high-throughput techniques to refine genetic analysis and develop tools for rapidly interpreting gene expression data.

In many of the new areas, the microarray and gene chip were tools for advancing a program of "molecularizing" established disciplines. But this could not be accomplished by simply plugging in a microarray and reading off the results. New tools and even modifications of the gene chip itself had to be developed in order to assimilate the microarray to the research objectives of these several fields.

Multidisciplinary teams of researchers and collaboration between academic researchers and their industry partners proved essential to advancing the technology. The demand for alternatives greatly expanded the market for these research tools and, as we show below, created opportunities for other firms to enter the market. The top three categories citing these studies Biochemistry, Biotechnology, and Genetics were not surprising; they represented the areas DNA microarrays were squarely targeted to address.

However, the amount of interest generated around microarray research methods was quite striking. When first released there was much concern regarding the reliability of the chips, quality control issues in manufacturing them, and how to interpret results of microarray experiments.

In some cases it was difficult to reproduce the results of experiments based on DNA chips. In addition, researchers discovered that each manufacturer's DNA microarray had its relative strengths and weaknesses; finding the right chip for the job was and still is of significant concern.

Many studies were done both to address a particular research question and to learn something about how to better use gene chips. The next major adopters of microarrays were those investigating cancer and cell biology. Often, these studies involved comparing the expression of thousands of genes in tumor cells to their expression in non-tumor cells. The same type of cancer e. It is often necessary to capture the broad set of involved genes including those regulating expression and their interplay to begin to profile particular cancers.

An understanding of the processes in cancerous cells aids in designing future drugs to disrupt the chain of events. More immediately, gene expression profiling of a particular patient's tumor through diagnostics, the genes for which are often selected by expression analysis with high density microarrays, enables prediction of the efficacy of existing treatments.

Thus, microarrays enabled comparative study of gene expression in cells that led to insights about the complex processes behind cancer progression, but they also allowed for research on selecting patient-specific treatments based on gene expression profiles in tumor cells. Some of the biological and medical fields affected by microarray research raise equally interesting issues. Microarrays enabled a broad range of researchers to better address questions such as how certain genes and their expression are related to the processes involved in particular diseases, to development and aging, and to the workings of the brain.

Microarrays could also be used to address questions on evolution. In other words, microarrays not only provided a valuable tool to these researchers; in certain cases, they made genetics more relevant to their respective fields than it had been previously, and in particular, to their methods of inquiry. There were also technical fields that took up research on DNA microarrays not for purposes of applying them within the field but in order to improve them and to provide better methods for interpreting gene expression data.

Physicists, chemists and various kinds of engineers created custom microarrays, labeling systems for genetic material and systems for reading gene chips, or they explored new methods of manufacturing arrays.

Interestingly, the sheer volume of data generated by gene expression studies forced geneticists, biologists, and others using microarrays to pull statisticians, mathematicians, and computer scientists into their research teams. Methods of reading, visualizing, and interpreting gene expression information and linking it to existing scientific knowledge became codified in a plethora of computer programs from in-house statistics and visualization tools at universities to major software suites developed by corporations that can be connected to online repositories of biological information.

In a network view, they would represent a major forward-linking hub that collapses a question addressed within the authors' traditional field of study into a problem solvable with microarrays and motivates a flurry of subsequent research in that new domain. For example, statistical analysis of gene expression data has become a major topic of research at many universities; as the table shows, one study that used statistical methods to evaluate microarray data, despite being published in , was cited over times.

While some of these articles' citations simply reflect acknowledgement of the new technology being applied in some fashion, many aim at expanding the capabilities of microarray technologies by addressing fundamental questions in an existing research domain. Nonetheless, both types of citations indicate the growing relevance of gene expression and other microarray based studies on various scientific fields.

Below we chart the rising use of microarray technologies and research underpinned by microarrays through counts of microarray related studies [Note S] by subject according to the Scopus database and by departmental affiliation of at least one of the authors.

We are particularly interested in highlighting the growth of use in particular disciplines in addition to biology, biochemistry, and genetics, and in illustrating how microarrays became relevant to a host of fields that could benefit from a better understanding of gene processes. In addition, we try to demonstrate that non-biological fields such as computer science became involved in order to enhance the microarray research itself.

While neither subject classifications nor departmental affiliations provide a definitive account of the story of microarrays in these fields, we believe that the two approaches to tracking diffusion reinforce one another and at a minimum point to the growing relevance of large scale gene expression monitoring technologies in various academic disciplines.

As we have discussed above, microarrays were not a simple tool biologists and geneticists could readily apply to understanding the role of particular genes. They often had to enlist the support of colleagues in other departments to analyze, view, and interpret the data provided by DNA microarrays. In addition, research to improve numerous aspects of gene chip experiments took hold in departments outside of the biological sciences. In addition, biological fields that still had to profit significantly from the results of mapping the human genome and myriad studies on individual genes were now able to better link existing research questions to genomics questions.

Although Affymetrix has dominated the commercial market for DNA microarrays since its inception, distantly followed by Agilent, it is important to note that nearly half of scientists using microarray systems had built them locally according to plans similar to those made available by Pat Brown and his colleagues at Stanford. While these generally offered less reliability, consistency, and had fewer applications, they were far cheaper than the commercially available systems.

Alongside the research universities and other non-profit institutions that had begun in incorporating gene chips into their research programs, many companies were looking at how to enter the gene chip business and how to build complementary systems. Interestingly, many of these efforts, particularly at the smaller companies were offshoots of the university research that had begun earlier on some aspect of gene chip applications or technologies, such as bioinformatics software.

Larger companies often stepped in by applying existing expertise and familiar manufacturing techniques to building their own versions of DNA microarrays. In the rightmost column we indicate whether the organization received any types of government grants for its research in this area.

Twenty-five of the forty organizations listed received government grants note the presence of universities, which depend heavily on government funding for all their research, and large companies, which rarely receive funding for this type of research. In the case of smaller, recently formed companies, 20 out of 28 received government funding. While the data is limited, it appears that new companies building technologies around microarrays were heavily supported by the federal government, helping to broaden the applications and power of the technology.

Our case studies were chosen to explore different perspectives on the issues we have defined as salient features of the networked, symbiotic structure supporting innovation in technology regions such as the Silicon Valley; namely, the role of federal support, the ability of companies to draw upon universities to provide expertise in addressing challenging scientific questions or help them couple their existing systems to new technologies, and the ability of commercially viable technologies generated by high-tech companies to attract government funding and shape entirely new academic research directions.

It might be argued that Affymetrix is a special case since, with its star-cast of consulting scientists, engineers, and successful entrepreneurs it was so remarkably positioned to take optimal advantage of the networks supporting innovation.

To address such concerns we chose four case studies that represent different trajectories microarray technology could take. Affymetrix was a startup. But what about a large, well established firm with large internal resources to devote to developing its own technology for entry in the microarray market?

Would it act independently of the network? Or would it draw upon the same regional networks as Affymetrix in developing its own microarray platform? What sorts of factors would motivate it to enter the market, and what sorts of resources would it draw upon? The case of Agilent, daughter firm of Silicon Valley giant Hewlett-Packard provides a striking opportunity to explore these issues. More importantly though, these case studies will help to illustrate in detail how a viable infrastructure of scientific research and complementary technologies emerged in the case of DNA microarrays, motivating universities, industry, and government, each in different ways, to pursue competitive scientific and commercial opportunities in the emerging microarray landscape.

But gene chips or DNA microarrays turn out to be only one possible application of microarrays. In the case of Symyx we explore how researchers — indeed researchers intimately connected with the original microarray project at Affymax — seized the opportunity to launch a new company that developed the basic idea of the original microarray to vigorously pursue combinatorial chemistry in the direction of non-organic materials science.

Quantum Dot provides an example of a "classic" university startup coming out of an entirely different technical domain, nanocrystals, and seizing an opportunity to incorporate its technology as a component in the DNA microarray system.

Our final case, Perlegen, is a spinoff of Affymetrix itself, focused on lines of research aimed at extending basic Affymetrix technology in ways directly relevant to concerns of the pharmaceutical industry. Together these case studies show that once microarrays got off the ground, players such as these made the technology an expansive and self-sustaining force.

Agilent got its start in the microarray business through a collaboration to build scanners for Affymetrix in the mid s, but the company decided to compete against its business partner in Through its other bio-analysis and lab products and its connections to HP's printing and scanning business, it already housed much of the expertise necessary to create its own version of a DNA microarray and the associated hardware and software.

To get a sense of how Agilent shifted its research to address the microarray market, we searched Agilent and HP's patent portfolios for inventions pertaining to microarray systems [Note V]. Most of these employees from other companies came from biotech firms such as Applied Biosystems, Caliper, and Abaxis, with the exception of an imaging expert from Polaroid. We also uncovered two ongoing faculty consultations, one with University of Colorado's Marvin Caruthers, a well-known biochemist whose former student Douglas Dellinger had been hired at the company, and another with Karin Caldwell, a biochemical surfaces expert at the University of Utah and Uppsala University in Sweden.

These hires from the biotech sector and the ongoing connections with academia may have served as means for Agilent to enhance its absorptive capacity [ 33 ], as companies such as Affymetrix have benefit significantly through collaborations with academics who help them integrate state of the art knowledge from different fields into their technology.

It also cannot be ignored that Agilent may have taken much longer in entering the business or may have never gotten started in microarrays had it not been for Affymetrix's lead. While Agilent did not receive government support for its research in this area, it did engage in many of the other formal relationships that characterize a networked innovative firm, such as partnering with Affymetrix early on for scanners and then collaborating with and investing in Rosetta Inpharmatics founded by Stephen Friend, formerly a faculty member of Harvard Medical School; Leland Hartwell, Nobel Prize in Medicine in ; and Leroy Hood, then chair of the molecular biotechnology department at the University of Washington.

Led by Alan Blanchard and Leroy Hood, Rosetta had devised an early ink-jet microarrayer based on Epson printers prior to its collaboration with Agilent, but partnership resulted in the use of Agilent ink-jet arrayers and Rosetta's bioinformatics software.

Yet in filling the traditional role of a large company that quickly follows a startup into a new market sometimes referred to as the "fast second" , Agilent did help to lower the costs of using microarrays and offered a new set of feature choices to consumers such as ease of customizability that they may not have had with a single commercial gene chip provider.

Even a company as diversified and seemingly well-positioned to entering the microarray business as Agilent still received benefits from participating in this larger network of activity surrounding high-throughput gene expression monitoring technologies. In addition to the ongoing academic consultants it retained, the company sent its researchers to numerous scientific conferences and collaborated on several papers with academics.

Through early , Agilent researchers had appeared as authors on over forty microarray papers, most of which were in collaboration with academic institutions such as Duke, Stanford, the University of Southern California, Michigan University, Washington University, and NC State [Note X]. Although this is common in companies with large research divisions, we believe it is an often overlooked, key source of project ideas and technical guidance. While Agilent hired several people to work in its newly formed microarray business, the convergence of expertise from scanners, printers, software, microfluidics, and chromatography equipment toward complete microarray systems within its own organization can be seen in the research trajectories of its scientists.

Lead researchers at Agilent often acted as the central bridge gathering those with different backgrounds around microarray printers and scanning systems. These researchers themselves came from one specific field or another, such as ink-jet printing, lab instrumentation, or biochemistry, but their changing research foci can be seen in the patents they filed over years preceding Agilent's entry into the microarray business.

For example, HP researcher Michael P. Caren worked in ink-jet nozzles and cartridges in the early s and transitioned into creation of arrayers for genetic material by the late 90s. Many researchers exhibited a similar pattern coming from different areas of HP or outside companies and eventually coalescing around microarray technologies such as scanners, printers, and slides.

Agilent settled on a very precise ink-jet based approach to depositing strands of genetic material at specific sites. It also developed its own scanners and analysis software. Eventually, Agilent emerged as the number two microarray provider. Agilent's sixty nucleotide arrays offered very good consistency, while its microarrayers were easy to customize and provided rapid manufacture of DNA chips. Agilent's life sciences business includes microarrays, microfluidics, gas chromatography, liquid chromatography, mass spectrometry, informatics tools, and related reagents [ 34 ].

Despite the obvious complementarities with its existing businesses and seemingly privileged entry into microarray technologies through its partnership with Affymetrix, Agilent cannot be seen as a large company forging ahead on its own.

While it did have much of the expertise necessary to begin this business, Agilent still collaborated with universities and actively engaged with the scientific research community that used microarrays. The case of Agilent unexpectedly brings forth several themes we have been exploring in the history of microarray technologies. Although the company had a great deal of the requisite expertise in-house based on our analysis of researcher's patent histories and was well-positioned to begin its own microarray business particularly given its connection to Affymetrix through their scanner partnership , Agilent still found it useful to collaborate with startups, consult with universities, and engage with the larger scientific community in developing its technologies.

Agilent's strong ties to the research community and the converging technical concerns of its researchers underscore the importance of multidisciplinary collaborations and a dialogue with the university-based and other scientists in developing complete systems for gene expression studies.

Microarrays not only became a platform upon which various types of genomics and microbiological technologies were built; they also provided conceptual inspiration for the development of an analogous approach to non-biological materials discovery.

The chemicals industry faced many of the same combinatorial chemistry challenges that plagued pharmaceuticals: a staggering number of possible molecular combinations and painstakingly slow trial and error testing processes.

A method of quickly screening the candidate compounds suggested by combinatorial chemistry had been in need for years. Perhaps unsurprisingly, Peter G. Schultz, a founder of Affymax, the company that spun out Affymetrix, and at the time a professor of chemistry at Berkeley, initially conceived of the application of high-density microarray techniques to inorganic materials testing.

In the background of his first patent on a high throughput chemistry chip, Schultz and his co-inventors Xiaodong Xiang of UC Berkeley and Isy Goldwasser one of his students who became President of the newly founded Symyx explain the need for massive expansion of trial and error techniques then employed in materials discovery. Similar to the initial problem of screening drug compounds at Affymax, the inventors indicate that given the low level of scientific understanding of the properties of materials, the need for massive screening elements against combinations of other elements requires a massively parallel approach [ 35 ].

Because chemistry was insufficiently advanced to model combinations of materials and their resulting properties, it was necessary to create as many different mixtures as possible and then conduct numerous laboratory tests to determine their usefulness. The inadequate screening methods for such a large range of potential materials represented a serious bottleneck in materials discovery. The math governing how combinatorial chemistry had created a crisis of testing was clear to those in the field, as former Caltech professor Henry Weinberg, who had been hired into Symyx, and collaborator James Engstrom of Cornell write:.

The number of compounds that needed to be screened against one another was simply too great using traditional methods, particularly when each process was multiplied by the need to vary temperature and pressure for each reaction [ 37 ]. Moreover, this new screening system had to have two major phases, the first in which it simply measured the reactivity of various polymers and a second, in which it another battery of tests could assess the "chemical, optical, mechanical, and electronic properties of large arrays of materials" [ 38 ].

The conceptual similarities of measuring reactions at known sites between gene chips and the new materials discovery arrays were described in a Symyx patent relating to manufacturing techniques:. The oligonucleotide probes, in turn, are available to participate in a hybridization reaction with selected nucleic acid components of the sample.

Generally, this interaction of probe and sample relates to the utility of the components of the biological sample, such as the identity, concentration, purity or form of the components being sensed [ 39 ]. In comparison, in the case of Symyx arrays, an overhead infrared camera would detect how much heat had been emitted from each well, indicating to what extent the combined chemicals had reacted.

However, other Symyx disclosures were quick to point out that the analogies ended at the conceptual level. Although the microarray was the essential platform that motivated the formation of Symyx, the technical challenges of implementing a materials array were quite different from those posed by a DNA chip.

In the background of their early patent filings, regarding their technical approach to massive chemical screening and its dissimilarities to biological microarrays, Schultz and colleagues write:. While the technology for building the chips was different, the overall process of screening using the microarray platform in the cases of genes and chemicals was quite similar.

In their article explaining the process of materials screening employed by Symyx, Murphy et al. Figures reproduced with the kind permission of Symyx Technologies, Inc. Similar to the benefits derived from using massive gene-expression monitoring technologies, the advantage to users of the technology primarily came in the form of better targets for further research as opposed to stumbling in the dark or working with the few targets whose properties are known.

In the figure on the right, Murphy et al. This is largely analogous to the fields involved in each facet of DNA chip technology, with biology and biochemistry substituted for physics and physical chemistry. Indeed, research teams at Symyx often consist of chemists, physicists, engineers and programmers.

Apart from the two companies being based around a technology originally conceived of at Affymax, and sharing founders Schultz and Zaffaroni, the company maintained collaborations with academic institutions such as UC Berkeley and University Frechet and early on hired chemical engineering professor W. Henry Weinberg from Caltech to be executive vice president and chief technical officer.

Kenneth J. Nussbacher, a fellow and executive vice president at Affymetrix, sits on the board of Symyx. One government grant was aimed at discovering new materials for methanol fuel cells. Yet the greatest source of funding for the company came from its large industrial partners with whom it has ongoing materials discovery programs. Symyx has already helped to develop and a variety of materials for different purposes, including oil refining catalysts, chemicals for sensor applications, and polymers for personal care products.

Thus, Symyx's approach, which it claims is times faster and times cheaper than traditional research methods [ 43 ], allowed it to take the microarray platform into a variety of new sectors.

In this case, it was research and licensing partnerships with established firms who had a need for materials with particular properties. Unlike Affymetrix or Agilent, which sell chips and the associated systems to customers, Symyx can be seen as more of a service provider to large companies, with its income primarily stemming from licensing royalties, using the microarray platform and a team of experts with diverse backgrounds to discover materials based on clients' needs.

Symyx represented an important foray into new applications for the microarray platform. Developing and commercializing the technology drew upon the set of actors we have been discussing. With the key technology being developed by academics and remaining tightly woven into cutting-edge chemistry, physics, and engineering, Symyx developed a method of high throughput screening that appealed to a broad set of large customers in different industries.

Its academic background and long-term research partnerships with established firms account for a great deal of the success of Symyx's discovery tools and processes. We now turn to the history of a startup that provided improved components for the microarray platform rather than seeking to provide technological competition for the gene chip or to prove a new application for microarrays as the organizations in the prior case studies have.

In fact, this technology did not even come out of the labs of users of gene expression microarrays who wanted to enhance their performance; instead, it came from the field of quantum nanocrystals small groupings of electrons with properties similar to an atom that physicists and chemists had begun exploring seriously in the s. Having spent years fine-tuning these crystals, but struggling to find a compelling application for them, several research teams began pursuing their use as biological labels when they realized that they were the perfect size for attaching to organic molecules.

In a retrospective portion of an article, Paul Alivisatos of UC Berkeley, one of the major pioneers in using quantum dots as biological labels, explains that the technology emerged from a exciting area of research for many physicists because of the ability to control the energy properties of quantum dots by changing their size. After a period of refining techniques to maintain quantum dots in solution, researchers seized upon their potential biological applications because the entire structure was roughly the size of a protein [ 44 ].

Quantum dots, groupings of free electrons that have properties very similar to atoms, had been a subject of research inquiry for chemists and physicists beginning in the s. In the late s, researchers at several schools, including UC Berkeley, Indiana University, and MIT, realized that quantum dots could be used to replace fluorescent dyes used in microarray studies.

These dyes often did not provide sufficient contrast, particularly when genetic sequences were placed very close to each other as in high-density chips. Quantum dots offered greater clarity and because of their consistent and highly specific wavelength response, they offered potential to tag the genetic sequence with additional information. In September of , Science published back-to-back articles by the competing research groups from UC Berkeley and Indiana University on the use of quantum nanocrystals as a tracking tool for biological material.

As the two foundational articles explain:. Further, it establishes a class of fluorescent probe for which no small organic molecule equivalent exists. The tunability of the optical features allows for their use as direct probes or as sensitizers for traditional probes" [ 45 ]. In comparison with organic dyes such as rhodamine, this class of luminescent labels is 20 times as bright, times as stable against photobleaching, and one-third as wide in spectral linewidth.

These nanometer-sized conjugates are water-soluble and biocompatible [ 46 ]. Quantum dots offered dramatically smaller, clearer, and more descriptive tags for biological molecules. Because quantum dots could be tuned to a variety of wavelengths, they could be used to represent much more information than the two dyes used in microarray experiments. In fact, this tunability and signal strength offered the potential for creating gene expression tests that did not even require probes to be placed at specific sites.

As explained in the background to a patent filed by the MIT quantum dot group, a particular nanocrystal could emit a wavelength corresponding to a specific gene sequence:. The system of the present invention, in contrast to fluorescently labeled probes used in the existing methods [DNA chips], is capable of not only acting as a probe for identification of a desired sequence, but is also capable of encoding information about the sequence itself.

Because the inventive identification system is capable of providing both a probe and identifier, ordered arrays are not necessary for accessing genetic information, although the inventive system can still be used in traditional arrays. Instead, a collection of beads, for example, can be assembled with the desired labeled DNA fragments, wherein said beads are also encoded with information about the particular sequence.

Upon binding, the oligo that hybridizes to the sample DNA can be detected by scanning the sample to identify the quantum dot labeled probe, while at the same time the sequence information can then be decoded by analyzing the quantum dot "barcode" [ 47 ]. While the initial opportunity for becoming involved with the rapidly emerging microarray market was attractive, the superior signal strength and ability to tune a probe to represent particular information had actually opened up countless tracking applications.

Paul Alivisatos and his collaborators were especially eager finally to apply and commercialize their longtime basic research in the form of a biological label. When Joel Martin, a serial entrepreneur and former chemist interested in the technology visited the Berkeley Lab in , the two began working out their ideas for a business, and founded Quantum Dot Corporation in [ 48 ]. It is likely the widespread importance of nanocrystal tags helped Quantum Dot Corporation attract government and venture capital funding.

The company received government grants before and after securing an impressive amount of venture capital funding. However, toxicity issues in the fundamental design of the tags delayed commercialization of the technology, seriously compromising its business prospects and its advantage as the first to commercialize this technology.

With the advent of new DNA sequencing technologies, some of the tests for which microarrays were used in the past now use DNA sequencing instead. But microarray tests still tend to be less expensive than sequencing, so they may be used for very large studies, as well as for some clinical tests.

To determine whether an individual possesses a mutation for a particular disease, a scientist first obtains a sample of DNA from the patient's blood as well as a control sample - one that does not contain a mutation in the gene of interest. The researcher then denatures the DNA in the samples - a process that separates the two complementary strands of DNA into single-stranded molecules. The next step is to cut the long strands of DNA into smaller, more manageable fragments and then to label each fragment by attaching a fluorescent dye there are other ways to do this, but this is one common method.

Both sets of labeled DNA are then inserted into the chip and allowed to hybridize - or bind - to the synthetic DNA on the chip. If the individual does not have a mutation for the gene, both the red and green samples will bind to the sequences on the chip that represent the sequence without the mutation the "normal" sequence. If the individual does possess a mutation, the individual's DNA will not bind properly to the DNA sequences on the chip that represent the "normal" sequence but instead will bind to the sequence on the chip that represents the mutated DNA.

What is a DNA microarray? This was expanded to an analysis of more than human sequences with computer driven scanning and image processing for quantitative analysis of the sequences in human colonic tumors and normal tissue and then to comparison of colonic tissues at different genetic risk. The use of a collection of distinct DNAs in arrays for expression profiling was also described in , and the arrayed DNAs were used to identify genes whose expression is modulated by interferon.

These early gene arrays were made by spotting cDNAs onto filter paper with a pin-spotting device. The use of miniaturized microarrays for gene expression profiling was first reported in ,and a complete eukaryotic genome Saccharomyces cerevisiae on a microarray was published in Click Here to Know about a Legend Dr. Abdul Kalam.

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