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Ayalew Tefferi - One of the best experts on this subject based on the ideXlab platform.

  • primer on Medical Genomics part xiii ethical and regulatory issues
    Mayo Clinic Proceedings, 2004
    Co-Authors: Christopher C Hook, Eugene P Dimagno, Ayalew Tefferi
    Abstract:

    Ethics in the new Genomics era has become an increasingly complex subject that often arouses passion and confusion. Although 50 years have elapsed since the elucidation of the DNA molecule, the recent near-complete sequencing of the human genome has sharply accelerated the incorporation of genetics into the Medical mainstream. Along with these scientific advances, however, have surfaced challenges, liabilities, and issues regarding the processing and management of genetic information as they relate to core ethical principles such as respect for autonomy, beneficence, nonmaleficence, and justice. Institutions and state and federal governments have initiated systematic and preemptive measures in education, resource development, and protective legislation to address these cardinal ethical issues. Genetic research is also being scrutinized carefully by institutional review boards, an activity that should not be perceived as being adversarial but rather as a protective shield for investigators and research participants alike. Ultimately, it is hoped that Genomics medicine will diminish rather than enhance existing sex-, race-, and socioeconomic class-based inequities in health care access and delivery. This article describes some but not all aspects of the ethical, legal, and social implications of Genomics in clinical practice.

  • primer on Medical Genomics part viii essentials of Medical genetics for the practicing physician
    Mayo Clinic proceedings, 2003
    Co-Authors: Regina E Ensenauer, David A H Whiteman, Michael J Ackerman, Shanda S Reinke, David J Tester, Ayalew Tefferi
    Abstract:

    After the mapping and sequencing of the human genome, Medical professionals from essentially all specialties turned their attention to investigating the role genes play in health and disease. Until recently, Medical genetics was considered a specialty of minor practical relevance. This view has changed with the development of new diagnostic and therapeutic possibilities. It is now realized that genetic disease represents an important part of Medical practice. Achievements in cancer genetics, in the field of prenatal diagnostics (including carrier testing for common recessive disorders), and in newborn screening for treatable metabolic disorders reinforce the rapidly expanding role of genetics in medicine. Diagnosing a genetic disorder not only allows for disease-specific management options but also has implications for the affected individual's entire family. A working understanding of the underlying concepts of genetic disease with regard to chromosome, single gene, mitochondrial, and multifactorial disorders is necessary for today's practicing physician. Routine clinical practice in virtually all Medical specialties will soon require integration of these fundamental concepts for use in accurate diagnosis and ensuring appropriate referrals for patients with genetic disease and their families.

  • primer on Medical Genomics part vi Genomics and molecular genetics in clinical practice
    Mayo Clinic proceedings, 2003
    Co-Authors: Stephen M Ansell, Michael J Ackerman, John L Black, Lewis R Roberts, Ayalew Tefferi
    Abstract:

    An important milestone in Medical science is the recent completion of a "working draft" of the human genome sequence. The identification of all human genes and their regulatory regions provides the framework to expedite our understanding of the molecular basis of disease. This advance has also formed the foundation for a broad range of genomic tools that can be applied to Medical science. These developments in global gene and gene product analysis as well as targeted molecular genetic testing are destined to change the practice of modern medicine. Despite these exciting advances, many practicing clinicians perceive that the role of molecular genetics, especially that of Genomics, is confined primarily to the research arena with little current clinical applicability. The aim of this article is to highlight advances in DNA/RNA-based methods of susceptibility screening, disease diagnosis and prognostication, and prediction of treatment outcome in regard to both drug toxicity and response as they apply to various areas of clinical medicine.

  • primer on Medical Genomics part iii microarray experiments and data analysis
    Mayo Clinic Proceedings, 2002
    Co-Authors: Ayalew Tefferi, Eric D Wieben, Mark E Bolander, Stephen M Ansell, Thomas C Spelsberg
    Abstract:

    Genomics has been defined as the comprehensive study of whole sets of genes, gene products, and their interactions as opposed to the study of single genes or proteins. Microarray technology is one of many novel tools that are allowing global and high-throughput analysis of genes and gene products. In addition to an introduction on underlying principles, the current review focuses on the use of both complementary DNA and oligodeoxynucleotide microarrays in gene expression analysis. Genome-wide experiments generate a massive amount of data points that require systematic methods of analysis to extract biologically useful information. Accordingly, the current educational communication discusses different methods of data analysis, including supervised and unsupervised clustering algorithms. Illustrative clinical examples show clinical applications, including (1) identification of candidate genes or pathological pathways (ie, elucidation of pathogenesis); (2) identification of "new" molecular classes of diseases that may be relevant in disease reclassification, prognostication, and treatment selection (ie, class discovery); and (3) use of expression profiles of known disease classes to predict diagnosis and classification of unknown samples (ie, class prediction). The current review should serve as an introduction to the subject for clinician investigators, physicians and Medical scientists in training, practicing clinicians, and other students of medicine.

  • primer on Medical Genomics part ii background principles and methods in molecular genetics
    Mayo Clinic proceedings, 2002
    Co-Authors: Ayalew Tefferi, Eric D Wieben, Gordon W Dewald, David A H Whiteman, Matthew E Bernard, Thomas C Spelsberg
    Abstract:

    The nucleus of every human cell contains the full complement of the human genome, which consists of approximately 30,000 to 70,000 named and unnamed genes and many intergenic DNA sequences. The double-helical DNA molecule in a human cell, associated with special proteins, is highly compacted into 22 pairs of autosomal chromosomes and an additional pair of sex chromosomes. The entire cellular DNA consists of approximately 3 billion base pairs, of which only 1% is thought to encode a functional protein or a polypeptide. Genetic information is expressed and regulated through a complex system of DNA transcription, RNA processing, RNA translation, and posttranslational and cotranslational modification of proteins. Advances in molecular biology techniques have allowed accurate and rapid characterization of DNA sequences as well as identification and quantification of cellular RNA and protein. Global analytic methods and human genetic mapping are expected to accelerate the process of identification and localization of disease genes. In this second part of an educational series in Medical Genomics, selected principles and methods in molecular biology are recapped, with the intent to prepare the reader for forthcoming articles with a more direct focus on aspects of the subject matter.

Carlos Bustamante - One of the best experts on this subject based on the ideXlab platform.

  • human demographic history impacts genetic risk prediction across diverse populations
    American Journal of Human Genetics, 2017
    Co-Authors: Alicia R Martin, Christopher R Gignoux, Raymond K Walters, Genevieve L Wojcik, Simon Gravel, Mark J Daly, Benjamin M Neale, Carlos Bustamante
    Abstract:

    The vast majority of genome-wide association studies (GWASs) are performed in Europeans, and their transferability to other populations is dependent on many factors (e.g., linkage disequilibrium, allele frequencies, genetic architecture). As Medical Genomics studies become increasingly large and diverse, gaining insights into population history and consequently the transferability of disease risk measurement is critical. Here, we disentangle recent population history in the widely used 1000 Genomes Project reference panel, with an emphasis on populations underrepresented in Medical studies. To examine the transferability of single-ancestry GWASs, we used published summary statistics to calculate polygenic risk scores for eight well-studied phenotypes. We identify directional inconsistencies in all scores; for example, height is predicted to decrease with genetic distance from Europeans, despite robust anthropological evidence that West Africans are as tall as Europeans on average. To gain deeper quantitative insights into GWAS transferability, we developed a complex trait coalescent-based simulation framework considering effects of polygenicity, causal allele frequency divergence, and heritability. As expected, correlations between true and inferred risk are typically highest in the population from which summary statistics were derived. We demonstrate that scores inferred from European GWASs are biased by genetic drift in other populations even when choosing the same causal variants and that biases in any direction are possible and unpredictable. This work cautions that summarizing findings from large-scale GWASs may have limited portability to other populations using standard approaches and highlights the need for generalized risk prediction methods and the inclusion of more diverse individuals in Medical Genomics.

  • human demographic history impacts genetic risk prediction across diverse populations
    bioRxiv, 2016
    Co-Authors: Alicia R Martin, Christopher R Gignoux, Raymond K Walters, Genevieve L Wojcik, Simon Gravel, Mark J Daly, Carlos Bustamante, Benjamin M Neale, Eimear E Kenny
    Abstract:

    The vast majority of genome-wide association studies are performed in Europeans, and their transferability to other populations is dependent on many factors (e.g. linkage disequilibrium, allele frequencies, genetic architecture). As Medical Genomics studies become increasingly large and diverse, gaining insights into population history and consequently the transferability of disease risk measurement is critical. Here, we disentangle recent population history in the widely-used 1000 Genomes Project reference panel, with an emphasis on populations underrepresented in Medical studies. To examine the transferability of single-ancestry GWAS, we used published summary statistics to calculate polygenic risk scores for six well-studied traits and diseases. We identified directional inconsistencies in all scores; for example, height is predicted to decrease with genetic distance from Europeans, despite robust anthropological evidence that West Africans are as tall as Europeans on average. To gain deeper quantitative insights into GWAS transferability, we developed a complex trait coalescent-based simulation framework considering effects of polygenicity, causal allele frequency divergence, and heritability. As expected, correlations between true and inferred risk were typically highest in the population from which summary statistics were derived. We demonstrated that scores inferred from European GWAS were biased by genetic drift in other populations even when choosing the same causal variants, and that biases in any direction were possible and unpredictable. This work cautions that summarizing findings from large-scale GWAS may have limited portability to other populations using standard approaches, and highlights the need for generalized risk prediction methods and the inclusion of more diverse individuals in Medical Genomics.

  • population genetic history and polygenic risk biases in 1000 genomes populations
    bioRxiv, 2016
    Co-Authors: Alicia R Martin, Christopher R Gignoux, Raymond K Walters, Genevieve L Wojcik, Simon Gravel, Mark J Daly, Carlos Bustamante, Eimear E Kenny
    Abstract:

    Background: Genome-wide association studies (GWAS) have largely focused on European descent populations, and the transferability of these findings to diverse populations is dependent on many factors, including selection, genetic divergence, heritability, and phenotype complexity. As Medical Genomics studies become increasingly large and ethnically diverse, gaining clear insight into population history and genetic diversity from available reference panels is critically important. Results: We disentangle the population history of the widely-used 1000 Genomes Project reference panel, with an emphasis on underrepresented Hispanic/Latino and African descent populations. By leveraging haplotype sharing, linkage disequilibrium decay, and ancestry deconvolution along chromosomes in admixed populations, we gain insights into ancestral allele frequencies, the origins, rates, and timings of admixture, and sex-biased demography. We make empirical observations to evaluate the impact of population structure in association studies, with conclusions that inform rare variant association in diverse populations, how we use standard GWAS tools, and transferability of findings across populations. Finally, we show through coalescent simulations that inferred polygenic risk scores derived from European GWAS are biased when applied to diverse populations. Conclusions: Our study provides fine-scale insight into the sampling, genetic origins, divergence, and sex-biased history of admixture in the 1000 Genomes Project populations. We show that the transferability of results from GWAS are dependent on the ancestral diversity of the study cohort as well as the phenotype polygenicity, causal allele frequency divergence, and heritability. This work highlights the need for inclusion of more diverse samples in Medical Genomics studies to enable broadly applicable disease risk information.

  • Genomics for the world
    Nature, 2011
    Co-Authors: Carlos Bustamante, Francisco M De La Vega, Esteban G Burchard
    Abstract:

    Medical Genomics has focused almost entirely on those of European descent. Other ethnic groups must be studied to ensure that more people benefit, say Carlos D. Bustamante, Esteban Gonzalez Burchard and Francisco M. De La Vega.

Alicia R Martin - One of the best experts on this subject based on the ideXlab platform.

  • haplotype sharing provides insights into fine scale population history and disease in finland
    WOS, 2018
    Co-Authors: Alicia R Martin, Konrad J Karczewski, Sini Kerminen, Mitja I Kurki, Anttipekka Sarin, Mykyta Artomov
    Abstract:

    Finland provides unique opportunities to investigate population and Medical Genomics because of its adoption of unified national electronic health records, detailed historical and birth records, and serial population bottlenecks. We assembled a comprehensive view of recent population history (≤100 generations), the timespan during which most rare-disease-causing alleles arose, by comparing pairwise haplotype sharing from 43,254 Finns to that of 16,060 Swedes, Estonians, Russians, and Hungarians from geographically and linguistically adjacent countries with different population histories. We find much more extensive sharing in Finns, with at least one ≥ 5 cM tract on average between pairs of unrelated individuals. By coupling haplotype sharing with fine-scale birth records from more than 25,000 individuals, we find that although haplotype sharing broadly decays with geographical distance, there are pockets of excess haplotype sharing; individuals from northeast Finland typically share several-fold more of their genome in identity-by-descent segments than individuals from southwest regions. We estimate recent effective population-size changes through time across regions of Finland, and we find that there was more continuous gene flow as Finns migrated from southwest to northeast between the early- and late-settlement regions than was dichotomously described previously. Lastly, we show that haplotype sharing is locally enriched by an order of magnitude among pairs of individuals sharing rare alleles and especially among pairs sharing rare disease-causing variants. Our work provides a general framework for using haplotype sharing to reconstruct an integrative view of recent population history and gain insight into the evolutionary origins of rare variants contributing to disease.

  • haplotype sharing provides insights into fine scale population history and disease in finland
    bioRxiv, 2017
    Co-Authors: Alicia R Martin, Konrad J Karczewski, Sini Kerminen, Mitja I Kurki, Anttipekka Sarin, Mykyta Artomov, Johan G Eriksson, Tonu Esko, Giulio Genovese
    Abstract:

    Finland provides unique opportunities to investigate population and Medical Genomics because of its adoption of unified national electronic health records, detailed historical and birth records, and serial population bottlenecks. We assemble a comprehensive view of recent population history (≤100 generations), the timespan during which most rare disease-causing alleles arose, by comparing pairwise haplotype sharing from 43,254 Finns to geographically and linguistically adjacent countries with different population histories, including 16,060 Swedes, Estonians, Russians, and Hungarians. We find much more extensive sharing in Finns, with at least one ≥ 5 cM tract on average between pairs of unrelated individuals. By coupling haplotype sharing with fine-scale birth records from over 25,000 individuals, we find that while haplotype sharing broadly decays with geographical distance, there are pockets of excess haplotype sharing; individuals from northeast Finland share several-fold more of their genome in identity-by-descent (IBD) segments than individuals from southwest regions containing the major cities of Helsinki and Turku. We estimate recent effective population size changes over time across regions of Finland and find significant differences between the Early and Late Settlement Regions as expected; however, our results indicate more continuous gene flow than previously indicated as Finns migrated towards the northernmost Lapland region. Lastly, we show that haplotype sharing is locally enriched among pairs of individuals sharing rare alleles by an order of magnitude, especially among pairs sharing rare disease causing variants. Our work provides a general framework for using haplotype sharing to reconstruct an integrative view of recent population history and gain insight into the evolutionary origins of rare variants contributing to disease.

  • human demographic history impacts genetic risk prediction across diverse populations
    American Journal of Human Genetics, 2017
    Co-Authors: Alicia R Martin, Christopher R Gignoux, Raymond K Walters, Genevieve L Wojcik, Simon Gravel, Mark J Daly, Benjamin M Neale, Carlos Bustamante
    Abstract:

    The vast majority of genome-wide association studies (GWASs) are performed in Europeans, and their transferability to other populations is dependent on many factors (e.g., linkage disequilibrium, allele frequencies, genetic architecture). As Medical Genomics studies become increasingly large and diverse, gaining insights into population history and consequently the transferability of disease risk measurement is critical. Here, we disentangle recent population history in the widely used 1000 Genomes Project reference panel, with an emphasis on populations underrepresented in Medical studies. To examine the transferability of single-ancestry GWASs, we used published summary statistics to calculate polygenic risk scores for eight well-studied phenotypes. We identify directional inconsistencies in all scores; for example, height is predicted to decrease with genetic distance from Europeans, despite robust anthropological evidence that West Africans are as tall as Europeans on average. To gain deeper quantitative insights into GWAS transferability, we developed a complex trait coalescent-based simulation framework considering effects of polygenicity, causal allele frequency divergence, and heritability. As expected, correlations between true and inferred risk are typically highest in the population from which summary statistics were derived. We demonstrate that scores inferred from European GWASs are biased by genetic drift in other populations even when choosing the same causal variants and that biases in any direction are possible and unpredictable. This work cautions that summarizing findings from large-scale GWASs may have limited portability to other populations using standard approaches and highlights the need for generalized risk prediction methods and the inclusion of more diverse individuals in Medical Genomics.

  • human demographic history impacts genetic risk prediction across diverse populations
    bioRxiv, 2016
    Co-Authors: Alicia R Martin, Christopher R Gignoux, Raymond K Walters, Genevieve L Wojcik, Simon Gravel, Mark J Daly, Carlos Bustamante, Benjamin M Neale, Eimear E Kenny
    Abstract:

    The vast majority of genome-wide association studies are performed in Europeans, and their transferability to other populations is dependent on many factors (e.g. linkage disequilibrium, allele frequencies, genetic architecture). As Medical Genomics studies become increasingly large and diverse, gaining insights into population history and consequently the transferability of disease risk measurement is critical. Here, we disentangle recent population history in the widely-used 1000 Genomes Project reference panel, with an emphasis on populations underrepresented in Medical studies. To examine the transferability of single-ancestry GWAS, we used published summary statistics to calculate polygenic risk scores for six well-studied traits and diseases. We identified directional inconsistencies in all scores; for example, height is predicted to decrease with genetic distance from Europeans, despite robust anthropological evidence that West Africans are as tall as Europeans on average. To gain deeper quantitative insights into GWAS transferability, we developed a complex trait coalescent-based simulation framework considering effects of polygenicity, causal allele frequency divergence, and heritability. As expected, correlations between true and inferred risk were typically highest in the population from which summary statistics were derived. We demonstrated that scores inferred from European GWAS were biased by genetic drift in other populations even when choosing the same causal variants, and that biases in any direction were possible and unpredictable. This work cautions that summarizing findings from large-scale GWAS may have limited portability to other populations using standard approaches, and highlights the need for generalized risk prediction methods and the inclusion of more diverse individuals in Medical Genomics.

  • population genetic history and polygenic risk biases in 1000 genomes populations
    bioRxiv, 2016
    Co-Authors: Alicia R Martin, Christopher R Gignoux, Raymond K Walters, Genevieve L Wojcik, Simon Gravel, Mark J Daly, Carlos Bustamante, Eimear E Kenny
    Abstract:

    Background: Genome-wide association studies (GWAS) have largely focused on European descent populations, and the transferability of these findings to diverse populations is dependent on many factors, including selection, genetic divergence, heritability, and phenotype complexity. As Medical Genomics studies become increasingly large and ethnically diverse, gaining clear insight into population history and genetic diversity from available reference panels is critically important. Results: We disentangle the population history of the widely-used 1000 Genomes Project reference panel, with an emphasis on underrepresented Hispanic/Latino and African descent populations. By leveraging haplotype sharing, linkage disequilibrium decay, and ancestry deconvolution along chromosomes in admixed populations, we gain insights into ancestral allele frequencies, the origins, rates, and timings of admixture, and sex-biased demography. We make empirical observations to evaluate the impact of population structure in association studies, with conclusions that inform rare variant association in diverse populations, how we use standard GWAS tools, and transferability of findings across populations. Finally, we show through coalescent simulations that inferred polygenic risk scores derived from European GWAS are biased when applied to diverse populations. Conclusions: Our study provides fine-scale insight into the sampling, genetic origins, divergence, and sex-biased history of admixture in the 1000 Genomes Project populations. We show that the transferability of results from GWAS are dependent on the ancestral diversity of the study cohort as well as the phenotype polygenicity, causal allele frequency divergence, and heritability. This work highlights the need for inclusion of more diverse samples in Medical Genomics studies to enable broadly applicable disease risk information.

Mark J Daly - One of the best experts on this subject based on the ideXlab platform.

  • human demographic history impacts genetic risk prediction across diverse populations
    American Journal of Human Genetics, 2017
    Co-Authors: Alicia R Martin, Christopher R Gignoux, Raymond K Walters, Genevieve L Wojcik, Simon Gravel, Mark J Daly, Benjamin M Neale, Carlos Bustamante
    Abstract:

    The vast majority of genome-wide association studies (GWASs) are performed in Europeans, and their transferability to other populations is dependent on many factors (e.g., linkage disequilibrium, allele frequencies, genetic architecture). As Medical Genomics studies become increasingly large and diverse, gaining insights into population history and consequently the transferability of disease risk measurement is critical. Here, we disentangle recent population history in the widely used 1000 Genomes Project reference panel, with an emphasis on populations underrepresented in Medical studies. To examine the transferability of single-ancestry GWASs, we used published summary statistics to calculate polygenic risk scores for eight well-studied phenotypes. We identify directional inconsistencies in all scores; for example, height is predicted to decrease with genetic distance from Europeans, despite robust anthropological evidence that West Africans are as tall as Europeans on average. To gain deeper quantitative insights into GWAS transferability, we developed a complex trait coalescent-based simulation framework considering effects of polygenicity, causal allele frequency divergence, and heritability. As expected, correlations between true and inferred risk are typically highest in the population from which summary statistics were derived. We demonstrate that scores inferred from European GWASs are biased by genetic drift in other populations even when choosing the same causal variants and that biases in any direction are possible and unpredictable. This work cautions that summarizing findings from large-scale GWASs may have limited portability to other populations using standard approaches and highlights the need for generalized risk prediction methods and the inclusion of more diverse individuals in Medical Genomics.

  • human demographic history impacts genetic risk prediction across diverse populations
    bioRxiv, 2016
    Co-Authors: Alicia R Martin, Christopher R Gignoux, Raymond K Walters, Genevieve L Wojcik, Simon Gravel, Mark J Daly, Carlos Bustamante, Benjamin M Neale, Eimear E Kenny
    Abstract:

    The vast majority of genome-wide association studies are performed in Europeans, and their transferability to other populations is dependent on many factors (e.g. linkage disequilibrium, allele frequencies, genetic architecture). As Medical Genomics studies become increasingly large and diverse, gaining insights into population history and consequently the transferability of disease risk measurement is critical. Here, we disentangle recent population history in the widely-used 1000 Genomes Project reference panel, with an emphasis on populations underrepresented in Medical studies. To examine the transferability of single-ancestry GWAS, we used published summary statistics to calculate polygenic risk scores for six well-studied traits and diseases. We identified directional inconsistencies in all scores; for example, height is predicted to decrease with genetic distance from Europeans, despite robust anthropological evidence that West Africans are as tall as Europeans on average. To gain deeper quantitative insights into GWAS transferability, we developed a complex trait coalescent-based simulation framework considering effects of polygenicity, causal allele frequency divergence, and heritability. As expected, correlations between true and inferred risk were typically highest in the population from which summary statistics were derived. We demonstrated that scores inferred from European GWAS were biased by genetic drift in other populations even when choosing the same causal variants, and that biases in any direction were possible and unpredictable. This work cautions that summarizing findings from large-scale GWAS may have limited portability to other populations using standard approaches, and highlights the need for generalized risk prediction methods and the inclusion of more diverse individuals in Medical Genomics.

  • population genetic history and polygenic risk biases in 1000 genomes populations
    bioRxiv, 2016
    Co-Authors: Alicia R Martin, Christopher R Gignoux, Raymond K Walters, Genevieve L Wojcik, Simon Gravel, Mark J Daly, Carlos Bustamante, Eimear E Kenny
    Abstract:

    Background: Genome-wide association studies (GWAS) have largely focused on European descent populations, and the transferability of these findings to diverse populations is dependent on many factors, including selection, genetic divergence, heritability, and phenotype complexity. As Medical Genomics studies become increasingly large and ethnically diverse, gaining clear insight into population history and genetic diversity from available reference panels is critically important. Results: We disentangle the population history of the widely-used 1000 Genomes Project reference panel, with an emphasis on underrepresented Hispanic/Latino and African descent populations. By leveraging haplotype sharing, linkage disequilibrium decay, and ancestry deconvolution along chromosomes in admixed populations, we gain insights into ancestral allele frequencies, the origins, rates, and timings of admixture, and sex-biased demography. We make empirical observations to evaluate the impact of population structure in association studies, with conclusions that inform rare variant association in diverse populations, how we use standard GWAS tools, and transferability of findings across populations. Finally, we show through coalescent simulations that inferred polygenic risk scores derived from European GWAS are biased when applied to diverse populations. Conclusions: Our study provides fine-scale insight into the sampling, genetic origins, divergence, and sex-biased history of admixture in the 1000 Genomes Project populations. We show that the transferability of results from GWAS are dependent on the ancestral diversity of the study cohort as well as the phenotype polygenicity, causal allele frequency divergence, and heritability. This work highlights the need for inclusion of more diverse samples in Medical Genomics studies to enable broadly applicable disease risk information.

Utkan Demirci - One of the best experts on this subject based on the ideXlab platform.

  • drop on demand single cell isolation and total rna analysis
    PLOS ONE, 2011
    Co-Authors: Sangjun Moon, Yungon Kim, Lingsheng Dong, Michael Lombardi, Edward Haeggstrom, Roderick V Jensen, Lili Hsiao, Utkan Demirci
    Abstract:

    Technologies that rapidly isolate viable single cells from heterogeneous solutions have significantly contributed to the field of Medical Genomics. Challenges remain both to enable efficient extraction, isolation and patterning of single cells from heterogeneous solutions as well as to keep them alive during the process due to a limited degree of control over single cell manipulation. Here, we present a microdroplet based method to isolate and pattern single cells from heterogeneous cell suspensions (10% target cell mixture), preserve viability of the extracted cells (97.0±0.8%), and obtain genomic information from isolated cells compared to the non-patterned controls. The cell encapsulation process is both experimentally and theoretically analyzed. Using the isolated cells, we identified 11 stem cell markers among 1000 genes and compare to the controls. This automated platform enabling high-throughput cell manipulation for subsequent genomic analysis employs fewer handling steps compared to existing methods.