TY - JOUR
AU1 - Cheng, Jerry C.
AU2 - Horwitz, Edwin M.
AU3 - Karsten, Stanislav L.
AU4 - Shoemaker, Lorelei
AU5 - Kornblumc, Harley I.
AU6 - Malik, Punam
AU7 - Sakamoto, Kathleen M.
AB - Stem cells, RNA interference, Expression profiles, Neural stem cells, Mesenchymal stem cells, Purification Gene therapy, Hematopoietic stem cells Introduction This is a meeting report on the workshop “New Technologies in Stem Cell Research,” which was presented to pediatric residents, fellows, and faculty at the Society for Pediatric Research meeting in San Francisco, California, on April 29, 2006. Four speakers presented an overview of selected topics related to the current status of methods used to study stem cells. The topics presented at the workshop focused on RNA interference, mesenchymal stem cells, expression analysis, and gene therapy. In the first report, Drs. Jerry Cheng and Kathleen Sakamoto summarize the application of RNA interference in stem cells. Second, Dr. Edwin Horwitz describes basic approaches to the isolation and purification of mesenchymal stem cells. Third, Drs. Stanislav Karsten, Lorelei Shoemaker, and Harley I. Kornblum discuss methods in expression analysis of stem cells. Fourth, Dr. Punam Malik reports on the use of gene therapy for hemoglobinopathies using autologous stem cells. RNA Interference and Stem Cells Jerry C. Chenga, Kathleen M. Sakamotoa,b,c aDivision of Hematology/Oncology, Department of Pediatrics, Gwynne Hazen Cherry Memorial Laboratories and Mattel Children's Hospital, Jonsson Comprehensive Cancer Center and bDepartment of Pathology and Laboratory Medicine, David Geffen School of Medicine at University of California Los Angeles, Los Angeles, California, USA; cDivision of Biology, California Institute of Technology, Pasadena, California, USA ABSTRACT RNA interference (RNAi) is a powerful tool with which to study gene function, especially in stem cells. Small interfering RNAs (siRNAs) can effectively be introduced either with a vehicle or through viral vectors to transiently or stably inhibit the expression of a particular gene target. Much is known about the optimization of siRNAs and method of delivery in mammalian cells. In this review, we discuss design considerations for siRNAs, methods of delivery, optimization of siRNAs, applications to study genes in stem cells, therapeutic applications, and remaining hurdles. With recent advances in RNAi, it is likely that application of this technology will increase in the future. Key Words. RNA interference • Stem cells • Lentivirus Introduction RNA interference (RNAi) describes the inhibition of gene expression by double-stranded RNAs (dsRNAs) developed in the mid-1990s [1]. Guo and Kemphues discovered that sense RNA was as effective as antisense RNA for suppressing gene expression in nematode worms (Caenorhabditis elegans) [2]. This was followed by the introduction of dsRNA into worms. When single-stranded antisense RNA and double-stranded RNA were introduced into worms, it was found that dsRNA was more effective than either strand individually in downregulating genes [1]. RNAi is a multistep process that involves the generation of small interfering RNAs (siRNAs) in vivo through the activity of the RNase III endonuclease Dicer. The resulting 21− to 23-nucleotide (nt) siRNAs mediate degradation of their complementary RNA [3]. It is now thought that RNAi induces gene silencing through various mechanisms. One is by sequence-specific targeted gene silencing. The second is through translational repression (microRNAs). Finally, it has been reported that RNAi maintains silenced regions of chromosomes [3]. Basic Mechanisms of RNAi Long dsRNAs are the precursors of the siRNAs that trigger the RNAi effect. When dsRNAs enter cells, they are cleaved by an RNase III-like enzyme known as Dicer into siRNAs (Fig. 1). These 21–23-nt siRNAs form part of a siRNA− protein complex known as RNA-induced silencing complex (RISC), which contains helicase activity that unwinds the two strands of RNA molecules, allowing the antisense strand to bind the targeted RNA [4–7]. RISC also has endonuclease activity that hydrolyzes the target RNA at the site where it binds the antisense strands. Formation of RISC is critical for mRNA degradation. Therefore, the RISC complex mediates the sequence-specific degradation of the target RNAs that contain homologous sequences to the siRNA. Figure 1. Open in new tabDownload slide siRNA pathways that target mRNA for degradation. Abbreviations: dsRNA, double-stranded RNA; RISC, RNA-induced silencing complex; shRNA, short hairpin RNA; siRNA, small interfering RNA. Figure 1. Open in new tabDownload slide siRNA pathways that target mRNA for degradation. Abbreviations: dsRNA, double-stranded RNA; RISC, RNA-induced silencing complex; shRNA, short hairpin RNA; siRNA, small interfering RNA. What Is a Desirable Target for RNAi? Desirable targets of RNAi include genes that are amplified or overexpressed in cells leading to a specific phenotype. Additional targets include aberrant proteins that are encoded by dominant mutant alleles. An example is oncogenes that produce transformation in mammalian cells. However, genes that are abundantly expressed or have a prolonged half-life may not be efficiently inhibited. Similarly, genes that are redundant may not be effectively downregulated. The advantages of RNAi are that the targeted degradation is very specific and can result in variable levels of downregulation such that gene dosage effects can be studied. This technology is much easier, quicker, and less expensive than generating knockout mice. RNAi can also be used to inhibit expression of multiple genes at the same time [8–10]. Design of siRNA The use of siRNAs has become a common method of downregulating gene expression to screen gene function in many cell types, including stem cells. Although long dsRNAs (>30 nt) are effective in suppressing gene expression in plants, Drosophila, and C. elegans, long dsRNAs are cleaved by Dicer to form siRNAs when introduced into mammalian cells, and these siRNAs lead to mRNA degradation. However, in mammalian cells, long dsRNAs activate the interferon response pathway, leading to nonspecific mRNA degradation. The dsRNA-dependent protein kinase (PKR) is activated, resulting in nonspecific translational inhibition [11, 12]. Therefore, the usefulness of dsRNA in mammalian cells is limited. In general, 21–23-nt siRNAs are too short to activate the nonspecific dsRNA response pathway, but they are effective in inhibiting the expression of specific targets. There are several limitations of using this technology in mammalian cells. In fungi, plants, and worms, siRNAs can be replicated in vivo. In mammalian cells, siRNAs do not prime the synthesis of dsRNA to form additional siRNAs, which may explain why this technology is less effective [9]. Nevertheless, there are several examples in which siRNAs are effective in a variety of mammalian cell types, including stem and progenitor cells [1, 13]. Optimization of siRNAs in mammalian cells is dependent on several factors. One is the accessibility of the target sequence to the desired mRNA substrate. Previous reports have suggested that selecting a target sequence 100–200 nts away from the translational initiation sequence AUG of the gene is desirable [1]. However, successful inhibition of gene expression has also been reported for siRNAs targeting various sequences, including the 3′ untranslated region [14]. Targeting of the 3′ untranslated region is also useful if rescue experiments are to be performed. There is no reliable way to predict or identify the ideal sequence for siRNA. Several reports have suggested that sequences that form the stems of the hairpin siRNAs, the loop size, and the sequences at the base of the loop might also affect siRNA-induced gene inhibition. Other determinants include thermodynamic stability; siRNA with lower thermodynamic stability for base pairing at the 5′ end of antisense (guide) strand and in the middle of the siRNA were more effective at RNAi than those that had stronger base pairings in these regions due to affects on uptake of guide strand into RISC and enhancing RISC binding to target mRNA. The sequence of siRNAs should be carefully designed. The number of nucleotides should be between 19 and 23. The GC content should be between 30% and 50%. The preferred format is AAN19TT. Sequence specificity to at least two nucleotides should be confirmed by Blast comparison of the National Center for Biotechnology Information GenBank database. Finally, one should query against the single nucleotide polymorphism database [10]. Optimization of siRNA To ensure that the gene of interest is effectively downregulated by the siRNA, it is now recommended that at least three different siRNA sequences per target be designed [15, 16]. More robust knockdown of genes has been reported using this approach of creating “multiplicity” controls. Inhibition of expression has been reported for up to 5–10 days when using “pools” of siRNAs in transfected cells. siRNA concentrations must also be optimized. In general, concentrations of siRNAs greater than 100 nM are considered to be toxic. Various amounts of siRNAs should be tested for each specific cell type. This should be considered when one is using multiple siRNA sequences. Multiple cell lines should also be tested to validate response and downregulation. Finally, a nucleotide Blast search should be performed to determine whether the siRNA sequence would target another gene. In terms of controls, scrambled or mutated sequences (http://www.sirnawizard.com) and unrelated genes (e.g., luciferase) are commonly used. To validate successful downregulation of the target gene, it is recommended that a Western blot analysis be performed to assess protein levels and Northern blot analysis or reverse transcription-polymerase chain reaction (RT-PCR) to measure RNA levels. Demonstration of lower mRNA levels is critical to rule out a microRNA effect and translational inhibition of gene expression. To control for off target effects, one can measure interferon response genes, including OAS1, OAS2, and INFB1, by RT-PCR [1]. Delivery of siRNA to Cells In mammalian cells, efficiency of siRNA to cells transiently depends on the vehicle or mode of delivery and the cell types. Approaches to introduce siRNAs into cells include a lipid-based vehicle (e.g., Lipofectamine) or a non-lipid-based approach (e.g., calcium phosphate or electroporation). The disadvantages of this approach are that the siRNAs are nonrenewable and are only effective as long as they are bath-applied to cells. An alternative strategy has been to deliver siRNAs through a DNA vector-mediated RNAi approach. Because of the transient nature of gene silencing produced by oligonucleotide siRNAs and their high costs of chemical synthesis, alternative approaches to introduce siRNAs in plasmid vectors have been developed. A variety of expression vectors are now available. Expression is driven by either the U6 (small nuclear RNA) or H1 RNA polymerase III promoters to drive expression of sequence-specific short hairpin RNAs (shRNAs) in mammalian cells [2]. These systems are based on the expression of siRNAs either as two separate strands or as a single shRNA. It is thought that the shRNAs are processed by Dicer to active siRNAs in vivo [17–19]. For stable expression in stem cells, successful delivery has been demonstrated with viral vectors. Various recombinant viral vectors have been developed to deliver shRNAs in mammalian cells [10, 20]. Lentiviral vectors are especially effective. The reasons for this are that lentiviruses have broader tropism and receptor-independent delivery, that they have the ability to integrate into the genome for stable gene silencing, and that lentiviral transduction and expression of shRNAs do not require cell division for integration into the genome [21]. Lentiviral transduction has been successfully performed in cell lines, mouse hematopoietic stem cells (HSCs), and embryonic stem (ES) cells [22–24]. Adenoviral vectors have also been reported to be useful for delivering siRNAs to target cells. This vector system has been used to downregulate genes in liver. However, this vector system has limited utility in stem cells, since low transduction rates have been found in ES cells and HSCs. This is most likely due to the fact that the receptor for adenovirus is not highly expressed in stem cells [25]. Similarly, adenoviral-associated vectors have been successfully used to deliver RNAi to nonstem cells [1]. If the stable transfection or transduction of siRNAs results in toxic effects to cells, an alternative approach is to use the inducible expression of shRNAs. The tetracycline/doxycycline regulated form of U6 or H1 promoter has been successfully used. If there is leakiness, other inducible systems, such as an ecdysone-inducible system, are more tightly regulated with less background. A newer approach has been a CRE-lox-inducible system [26]. Most recently, a doxycycline-inducible vector that contains a KRAB domain from one-third of zinc finger domains was used in cell lines, mouse ES cells, epithelial breast cancer cells, rat brains, CD34+ cells, and transgenic mice [27]. Application of RNAi in Stem Cells There is now emerging evidence that RNAi can be used to study gene function and for therapeutic application. ES cells are pluripotent stem cells that are derived from the inner cell mass of the 3.5-day-old mouse blastocyst [1, 28]. These cells are desirable models to study the regulation of development and cell lineage commitment and differentiation, since ES cells can give rise to all three germ layers. This system is a powerful tool with which to study development. Interestingly, long dsRNA has been used in ES cells, but only when undifferentiated. The reason for this is unknown. In differentiated ES cells, siRNAs have been found to be effective in inhibiting genes, such as PU1 and c-EBPa [1]. A variety of other genes have been downregulated in ES cells, such as Shp-2 and Oct-4. Synthetic shRNAs recently have been shown to be efficiently transfected transiently with Lipofectamine [29]. More commonly, viral vector systems have been used to transduce genes of interest for stable expression of shRNAs. HSCs are a self-renewing population of cells in the bone marrow that gives rise to all differentiated hematopoietic cells [1]. A number of genes have been targeted using RNAi in HSCs. Growth factor receptor genes, clusters of differentiation, chemokines, oncogenes (bcr-abl), tumor suppressors, human immunodeficiency virus genes, globin genes, and RPS19 expression have all been successfully targeted. In most cases, retroviral or lentiviral vector systems were used. Electroporation has been used successfully to introduce dsRNA in HSCs [13]. Lipofectamine has also been reported to effectively transfect oligonucleotide siRNAs into hematopoietic progenitor cells [30]. HSCs that are transduced with shRNAs can then be studied in vitro using methylcellulose colony assays or in vivo in bone marrow transplantation experiments. Neural Stem Cells and MSCs Neural stem cells (NSCs) have also been transduced with shRNAs to downregulate genes. Examples of genes inhibited in NSCs by RNAi are MELK, PPARγ and B27.a genes [31–33]. MSCs have been studied using both viral and nonviral methods. Genes inhibited using viral vectors were β-catenin, Msx2, and mecdin [2, 34]. Nonviral liposomal methods to introduce siRNAs into MSCs have been used to inhibit epidermal growth factor receptor and connective tissue growth factor [35, 36]. Recently, a transfection microarray approach was generated in which siRNAs were applied onto slides that are coated with poly-l-lysine and fibronectin. MSCs were then placed on top of the poly-l-lysine and siRNA sandwich. Fluorescent microscopy was used to then visualize and quantify the degree of downregulation [37, 38]. A similar approach was used with HeLa cells placed on slides treated with siRNAs, in which cells were then followed in real time using time-lapse fluorescent microscopy as a high-throughput method to screen for genes involved in chromosomal segregation [39]. shRNA Libraries One of the technological advances in the RNAi field has been the development of shRNA libraries to screen for genes that regulate a specific pathway or biological function. Many of the libraries rely on lentiviral vector-based expression. Libraries have been used to identify deubiquitinating enzymes [40], sensitivity to small molecule inhibitors, novel cancer genes, and previously unidentified components of signaling pathways. A recent report from the Broad/Massachusetts Institute of Technology group (The RNAi Consortium) used an shRNA library with 72,600 clones targeting 10,500 human and 5,300 mouse genes [41]. It is anticipated that the numbers of genes targeted could be as high as 15,000 human or mouse genes. Viruses expressing shRNAs can be transiently or stably transduced into mammalian cells [41]. Genes that are involved in a particular cellular process will be identified through identification of the shRNA clones that block the function of the gene. An inducible shRNA library has also been used recently to identify genes that regulate proliferation or survival of diffuse large B cell lymphoma cells to seek novel targets for therapy [42]. Therapeutic Applications of RNAi The field of RNAi is advancing at a rapid pace. The application of RNAi as gene therapy is now being realized. In mice, delivery of siRNA to downregulate Fas by hydrodynamic tail injection resulted in protection from fulminant hepatitis [43]. A recent report by Samakoglu et al. has demonstrated that sickle globin gene can be downregulated in CD34+ cells using a lentiviral shRNA, with a concomitant increase in γ-globin expression in erythroid-specific manner [44]. Another advance has been the successful RNAi-mediated gene silencing in nonhuman primates. The first report of systemic delivery of APOB siRNA in nonrodent species was recently reported [45]. APOB is a component of low-density lipoprotein (LDL) and regulates the storage and metabolism of cholesterol. A liposomal formulation of APO-B siRNAs was intravenously administered into cynomolgus monkeys with effective inhibition of APOB levels after 48 hours and 11 days. Plasma levels demonstrated that not only LDL and cholesterol levels were lower than controls, but high-density lipoprotein levels were not affected. Although previous success was shown with hydrodynamic tail injection of oligonucleotide siRNAs in rodents, this was the first report of siRNAs successfully targeting a gene in nonrodent models. Remaining Challenges Although the field of RNAi has progressed rapidly, there are several hurdles that remain before this technology can be fully applied in humans. The specificity and toxicity of siRNAs must be more rigorously examined. The use of lentiviral vectors in gene therapy has led to insertional mutagenesis and malignancies, which must be overcome. Newer generations of lentiviral vectors are currently being studied. Stability of siRNAs is also problematic for long-term use. However, recent advances in nanotechnology have demonstrated that delivery of siRNAs using nanoparticles has potential in the clinics [46]. Given the advances in the field, it is highly likely that within the next few years, RNAi will become a viable approach to treat human disease. Acknowledgments This research was supported by grants from the NIH (CA108545, HL 75,826, RHL083077A), the American Cancer Society (RSG-99-081-04-LIB), and the Department of Defense (CM050077). J.C.C. is funded by the NIH (Grant F32 HL085013-01A2). All authors contributed equally. Disclosure of Potential Conflicts of Interest The authors indicate no potential conflicts of interest. Fundamentals of MSC Isolation and Purification Edwin M. Horwitz Department of Bone Marrow Transplantation, St. Jude Children's Research Hospital, Memphis, Tennessee, USA Key Words. Mesenchymal stem cells • Purification • Isolation Introduction Mesenchymal stromal cells are the spindle-shaped adherent cells isolated from bone marrow and other tissues [1, 2]. Designated mesenchymal stem cells by some investigators, these cells are increasingly being investigated as cell therapy to rebuild diseased or damaged tissues [3–6] and as immunomodulatory therapy for the treatment of graft-versus-host disease [7] and autoimmune disorders [8, 9]. It is quite important, then, to understand the various approaches of isolation purification and fundamental characterization of these potentially powerful therapeutic cells. The notion of a stromal stem cell thought to repopulate the marrow microenvironment in analogy to the hematopoietic stem cell that can repopulate hematopoiesis was proposed by Owen and Friedenstein [10], largely based on the work of Friedenstein et al. [11, 12]. This stem cell concept was extended to all mesenchymal tissues, and the term “mesenchymal stem cell” was popularized by Caplan, who pioneered much of our early understanding of this cells [13]. Indeed, mesenchymal stromal cells seem to function as stem cells in vitro. Our general concept of a stem cell evolved from our understanding of hematopoietic stem cell. Till and McCulloch suggested that the stem cell could be defined as a cell with extensive self-renewal capacity and the potential to terminally differentiate to two or more lineages [14]. Based on this definition, the mesenchymal stromal cells do, in fact, meet these criteria in vitro; however, true “stemness” is likely is much more complex and is most often operationally defined. As this idea has become increasingly recognized, many investigators suggested that convincing data supporting mesenchymal stromal cells as stem cells was lacking [15]. Hence, the Mesenchymal and Tissue Stem Cell Committee of International Society for Cellular Therapy has proposed that the term “mesenchymal stromal cell” is a more appropriate designation for this heterogeneous population of cells, maintaining the abbreviation “MSC” for “mesenchymal stromal cell” [16], while reserving the term “mesenchymal stem cell” for a subset of these (or other) cells that demonstrate stem cell activity in vivo by clearly stated criteria. Overview of the Isolation of Mesenchymal Stromal Cells All strategies to isolate mesenchymal stromal cells must take into account that the cells are quite rare within their tissue source. For example, mesenchymal stromal cells are estimated to comprise 0.01% of bone marrow mononuclear cells [17]. With this in mind, there are currently four principal strategies for the isolation of mesenchymal stromal cells. First, the cells can be isolated by “adherence selection,” in which the mesenchymal stromal cells are selected by their capacity for adherence to plastic in vitro. Second, the mesenchymal stromal cells can be selected by surface antigen expression using fluorescence-activated cell sorting (FACS). Third, surface antigen expression can be exploited to isolate mesenchymal stromal cells by magnetic label-activated cell sorting using antibodies conjugated to magnetic beads. Finally, populations of cells can be enriched for mesenchymal stromal cells by depleting the bone marrow cells of all other cells. Antibodies to non-mesenchymal stromal cell antigens can be conjugated to beads and then separated from the fraction of cells containing the mesenchymal stromal cells by centrifugation. This is not truly an isolation approach; rather, it is an enrichment of mesenchymal stromal cells within a still crude cell preparation. The mesenchymal stromal cell-enriched populations of cells must undergo a second isolation step, most often by adherence selection, to obtain mesenchymal stromal cells. Isolation of the Mononuclear Cells For the following discussion of the isolation of mesenchymal stromal cells, we will use bone marrow as the prototypic tissue since it is currently the most common source of mesenchymal stromal cells. The principles are equally applicable to other cell sources. In general, the first step to isolate mesenchymal stromal cells is to obtain mononuclear cells (MNCs) and rid the preparation of debris, typically by density centrifugation. Isolation of MNCs is important regardless of the subsequent approaches to obtaining a population of mesenchymal stromal cells. The two most common media for density centrifugation are Ficoll (1.077 g/cm3) and Percoll (1.073 g/cm3). Ficoll is frequently used to isolate bone marrow mononuclear cells and has also been extensively used in the isolation of mononuclear cells in anticipation of isolating mesenchymal stromal cells by adherent selection. Percoll may also be used to isolate mesenchymal stromal cells by two different approaches. First, a discontinuous gradient can be used, where the bone marrow mononuclear cells will band at the interface in a similar fashion as when using Ficoll. Alternatively, investigators can generate a continuous gradient with Percoll. In this case, the mesenchymal stromal cells will band at approximately 1.07 g/cm3. In practice, a large layer of Percoll is harvested from the centrifuge tubes to maximize the recovery of mesenchymal stromal cells [13]. Whether one medium in particular offers an advantage is unclear; thus, investigators should use the medium with which they have the most experience. Regardless of which medium is used, the resulting mononuclear cells must now undergo a further procedure, as stated above, to actually isolate the mesenchymal stromal cells. Adherence Selection The most common and best-characterized method to isolate mesenchymal stromal cells is by adherence selection. The mononuclear cells resulting from the density centrifugation are transferred to a tissue culture vessel and maintained at 37°C in a standard incubator for 3 days. Then, the medium is replaced, which removes the nonadherent cells. The typical MNC density is 1.65 × 105 cells per cm2 [18]; however, lower densities also generate an acceptable yield. Any type of plastic culture vessel (dish, flask, or multilayer “cell factory”) may be used with an equivalent recovery of mesenchymal stromal cells. Media Several media have been used for this initial mesenchymal stromal cell isolation and subsequent cell expansion. In our laboratory, we typically use Dulbecco's modified Eagle's medium, but α-modified Eagle's medium and McCoy's 5A also support the growth of mesenchymal stromal cells. McCoy's 5A culture medium may be preferable because it contains ascorbic acid (E. Clarke, personal communication); however, in our laboratory, we have not been able to demonstrate a significant difference among the various media. All synthetic media will require growth factor supplementation; most often, investigators simply add fetal bovine serum (FBS), as this has proved an effective medium supplement to support the growth and in vitro differentiation of mesenchymal stromal cells [19]. The standard practice is to supplement with 10% FBS; however, in the laboratory, 20% FBS often results in more prolific cell growth. Importantly, FBS preparations can differ in their potential to support mesenchymal stromal cells; therefore, investigators generally screen several lots of FBS to identify the product that best supports bone marrow stromal stem cells (CFU-F) formation and mesenchymal stromal cell propagation and purchase a large stock of that particular lot. Adult human serum, specifically autologous serum, has been reported to support the growth of human mesenchymal stromal cells [20, 21], although many laboratories are not able to reproduce these published results and consequently there are few scientific or clinical reports using autologous human serum. However, supplementing adult human serum with cytokines such as basic fibroblast growth factor, epidermal growth factor, and/or platelet-derived growth factor will support mesenchymal stromal cell growth [22, 23] (unpublished observation) and may be used if animal serum must be avoided. Serum-free medium has also been reported [22] but has not been extensively used by independent laboratories, and development of new serum-free media to isolate and expand mesenchymal stromal cells is an area of investigation within the biotechnology industry. Recently, human serum with a platelet lysate was shown to support mesenchymal stromal cells in culture [24]. Human serum containing a suspension of platelets can be frozen at −80°C and thawed just before the preparation of tissue culture media. The precipitate and other particulate matter must be removed by centrifugation. Then this serum/platelet lysate can be used to supplement (final volume, 5%) any of the synthetic media discussed above. There are currently few reports documenting the efficacy of serum/platelet lysate supplementation of media for mesenchymal stromal cell expansion, but the scientific community is currently showing great enthusiasm for this approach, especially when translating mesenchymal stromal cell-based therapy to the clinical setting, where the elimination of animal products may prove advantageous. Hence, it is likely that many studies will be forthcoming over the next few years. At this juncture, the MNCs have been placed into tissue culture and maintained in the medium of choice for 3 days, after which the medium should be replaced, which removes the nonadherent cells. The cells remaining adherent to plastic are the mesenchymal stromal cells. Other time intervals prior to the initial medium change have been used. Some investigators replace the medium in a few hours to 1 day, whereas others choose to wait up to 5 days. The longer intervals often result in a greater recovery of mesenchymal stromal cells but lesser initial purity. Conversely, shorter intervals result in a lesser mesenchymal stromal cell recovery but greater initial purity. Regardless of the time interval used, there remains significant non-mesenchymal stromal cell contamination of the cultures. Macrophages will also directly adhere to the plastic surface. Some cells, such as hematopoietic progenitors and mature B-cells, will adhere to the mesenchymal stromal cells. Thus, the mesenchymal stromal cell preparation will require a greater level of purity than that afforded by the initial isolation protocol. Further enrichment occurs as the mesenchymal stromal cells are cultured and passaged, as the other cell types do not expand to any appreciable extent. This can be demonstrated by flow cytometry. Other Isolation Methods Mesenchymal stromal cells may also be isolated by multiparameter FACS technology with select antibodies that can define mesenchymal stromal cells, which is a subject of some debate. Initially, the two antibodies SH2 and SH3 were used to identify the heterogeneous population of cells designated mesenchymal stromal cells [2, 13]. Although often used by many laboratories through the generosity of the original investigators, these antibodies were not commercially available. More recently, SH2 and SH3 were found to recognize epitopes on CD105 and CD73, respectively [25, 26]. In theory, then, CD45− CD105+ CD73+ marrow cells could be used to isolate mesenchymal stromal cells by FACS; however, since this approach is tedious and does not offer a proven advantage, it has not been used. The monoclonal IgM antibody STRO-1, developed by Simmons and Torok-Storb in 1991 [27], identifies a subset of human marrow cells that is composed of erythroid precursors and CFU-F cells [27]. In fact, most, or all stromal precursors seem to reside in the STRO-1 fraction of marrow cells; however, this population remains heterogeneous. STRO-1 has been extensively studied by Gronthos et al. [28] and Shi and Gronthos [29], who showed that two color isolation strategies using STRO-1+/CD106+ or STRO-1+/CD146+ yield a cell product highly enriched for high proliferative adherent cells. Whether these phenotypes represent a bona fide mesenchymal stem cell or a more highly enriched population of progenitors, as well as the biologic significance/therapeutic value of this phenotypically defined subset of cells, awaits broad independent scientific confirmation. Interested investigators can obtain STRO-1 through the Developmental Studies Hybridoma Bank (Iowa City, IA, http://www.uiowa.edu/∼dshbwww), which is under the auspices of the National Institute of Child Health and Human Development. Other antibodies have been used to isolate mesenchymal stromal cells. The antibody D7FIB recognizes mesenchymal stromal cells, and CD45− D7FIB+ marrow cells have been show to represent mesenchymal stromal cells [17, 30]. The low-affinity nerve growth factor receptor CD271 also recognizes mesenchymal stromal cells and can be used to prospectively isolate cells [31]. At recent scientific meetings focused on mesenchymal stromal cells in North America and Europe, CD271 seems to be gaining the interest of many clinician scientists. However, the value of CD271 selection is unproven. Currently, most investigators studying the potential applications of the heterogeneous population of mesenchymal stromal cells isolate cells by adherence selection. Investigators focused on the biologic properties of mesenchymal stromal cells, especially those investigators seeking a more homogenous population of cells or trying to define a purified population of stem cells, use more specific phenotypic criteria. There is still considerable debate on how best to define the heterogeneous population of mesenchymal stromal cells, as well as a putative mesenchymal stem cell. Hence, the importance of various phenotypic markers engenders extensive discussion and surface antigen expression as a means of isolating the cells is subject to some uncertainty. Similar to FACS isolation of mesenchymal stromal cells, antibodies conjugated to magnetic beads can be used to isolate mesenchymal stromal cells [31]. The cells, bound with magnetically labeled antibody, can be sorted by passing through a magnetic field. This so-called magnetic label-activated cell sorting is a highly effective method to isolate a wide variety of cells. However, the caveat of using specific antigens for FACS is equally applicable to magnetic label-activating cell sorting. Mesenchymal Stromal Cell Expansion The initial isolation of mesenchymal stromal cells, by any method, generally yields a relatively small population of cells. This finding is not surprising considering that mesenchymal stromal cells are minor constituents of most tissue sources (e.g., 0.01% of bone marrow MNCs). Hence, the mesenchymal stromal cells will require substantial culture expansion prior to most experimental applications. The medium used for the isolation of mesenchymal stromal cells is most often used throughout the culture expansion. The cells are maintained in tissue culture under standard conditions with medium replacement every 3–4 days. The cultures should be monitored often by visual examination with an invested microscope (daily if possible), and the cells should be passaged when the population attains approximately 80% confluence on the bottom of the tissue culture vessel [2]. The cells should not be allowed to contact each other, as this may alter the phenotype [2]. Mesenchymal stromal cells can be released from the vessel by trypsinization and then collected by pipette, washed, and replaced into a new culture vessel. Although most expansion protocols suggest a replating cell density of 2,000–4,000 cells per cm2 for general experimental applications, the optimal cell density depends, in part, on the desired outcome. A study of cell plating density showed that very low densities, as low as 2.5 cells per cm2, yield a significantly greater number of population doublings (expansion) than higher cell densities over a given time interval [32]. However, the total number of cells obtained at the end of the expansion is less. Thus, if clonal expansion is desired, very low plating cell densities are best, but if the goal is to obtain a large number of mesenchymal stromal cells, higher cell densities (e.g., 1,000–4,000 cells per cm2) may be preferable. The mesenchymal stromal cells may be expanded until the desired number of cells is attained; however, the expansion potential is not infinite (i.e., mesenchymal stromal cells will senesce in culture). Most studies use cells between passage 1 and passage 8. Purification As noted above, the isolation of mesenchymal stromal cells yields a preparation still “contaminated” with non-mesenchymal stromal cells. This seems to be true whether mesenchymal stromal cells are isolated by adherence selection or magnetic bead-based cell sorting. However, as the cells propagate and the cultures undergo further medium changes, the nonadherent cells will be removed. Thus, expansion is purification. Further purification of the mesenchymal stromal cells is generally not required; however, proving the lack of non-mesenchymal stromal cells (e.g., hematopoietic cells) is required. Such analyses are most readily accomplished with flow cytometry, demonstrating that cells expressing hematopoietic antigens are not present in the cell preparation. Characterization The final product of mesenchymal stromal cells should be characterized to prove the identity of the cells. Although mesenchymal stromal cells are clearly a heterogeneous population, the International Society for Cellular Therapy has suggested a working definition for mesenchymal stromal cells as (a) plastic adherent cells that (b) express CD105, CD73, and CD90 on the cell surface determined by flow cytometry and lack expression of CD45, CD34, CD11B or CD14, CD19, or CD79α and human leukocyte antigen-DR. The latter is most important to exclude hematopoietic contamination as a means of confirming purity rather than identity. Finally, the population of cells should (c) have the capacity for in vitro differentiation to osteoblast, adipocytes, and chondroblasts. Thus, adherence, surface antigen expression, and in vitro differentiation collectively define the heterogeneous population of mesenchymal stromal cells, and experimental data demonstrating these properties may be presented as evidence of mesenchymal stromal cells [33]. This definition is quite cumbersome, and a simpler defining phenotype is clearly needed; however, a single antigenic determinant (e.g., STRO-1 or CD271 [LNGRF]) to define the heterogeneous population has yet to gain universal acceptance in analogy to the biomedical scientific community's acceptance of CD34 expression as a marker of an enriched population of hematopoietic stem cells. Moreover, a combination of antigens to define subsets of mesenchymal stromal cells, or perhaps a mesenchymal stem cell, although reported [17, 28, 29, 31], is not yet widely accepted. Indeed, phenotypic analysis and correlation of the antigenic phenotype with biologic activity, especially in vivo activity, is an area of considerable effort within the field of mesenchymal stromal cell biology. Parting Thoughts This short primer has highlighted the fundamental features of the isolation and purification of mesenchymal stromal cells. Certainly, as technology advances, new methods will evolve and, hopefully, improve our efforts. Current methods of magnetic bead isolation will likely gain prominence as we better define the phenotype of mesenchymal stromal cells and cell subsets with unique biologic properties. New investigators can best gauge the state of the art by observing the methodology used by the preponderance of recent reports. Currently, there are many feasible approaches; the most important aspect of mesenchymal stromal cell isolation is to develop the protocols that work best in your laboratory. Disclosure of Potential Conflicts of Interest The authors indicate no potential conflicts of interest. Methods in Expression Analysis of Stem Cells Stanislav L. Karstena, Lorelei Shoemakerb, Harley I. Kornblumcc,d,e,f Departments of aNeurology, cPsychiatry, dPharmacology, and ePediatrics, bInterdepartmental Program in the Neurosciences, and fSemel Institute for Neuroscience, David Geffen School of Medicine at University of California Los Angeles, Los Angeles, California, USA Key Words. Genomics • Proteomics • Microarray • Large scale • Methods ABSTRACT Recent advances in stem cell technology have opened the door to the study of stem cell biology, including mechanisms underlying the fundamental properties of stem cells: self-renewal and cell fate. These analyses can be greatly enhanced by large-scale studies of gene and protein expression. Such studies can be used to categorize stem cells and their progeny, as well as to determine specific genes, proteins, and molecular pathways involved in functional processes. This review provides examples of how expression analysis can be used by the stem cell biologist, as well as methodological guidance in determining what questions can be asked. Furthermore, we provide descriptions of currently available microarray platforms and analysis tools. Introduction Stem cell biology is at an important stage in its development. Recent years have seen an explosion in the amount of information available regarding both adult and embryonic stem cells. Initial studies have documented both the existence and isolation of numerous cell types and have provided promising evidence that therapeutic strategies using stem cells may be possible. Subsequently, a number of investigators have begun to develop studies to unravel the molecular mechanisms of stem cell function, including studies of cell fate and self-renewal. In conducting these studies, many investigators are now using modern methods of large-scale, high-throughput characterization of stem cells and their progeny. Methods used can interrogate large percentages of the transcriptome or proteome and allow for a more detailed description of the cells and tissues being studied, as well as insight into fundamental mechanisms of stem cell biology. This review is meant to give the stem cell scientist an introduction to and an update on some of these methods, including what specific questions can be resolved through their use. Since gene expression microarray technology is the most commonly used approach, we will devote the greatest attention to this topic. What Types of Questions Can Be Answered with Transcriptome/Proteome-Wide Profiling? The advent of microarray and proteomic technologies opened the door for numerous types of studies. Early studies using microarrays made comparisons of gene expression of cells in two (or more) different states to compare what sets of genes are regulated during normal cellular processes, such as the cell cycle, or following a particular perturbation, such as a drug treatment. These studies generally compare large numbers of genes (or proteins) and determine which ones are significantly different between the two populations. Such design, although using heterogeneous cellular populations, can provide important clues about gene function, especially when dramatically different cell culture conditions are tested (e.g., growth factor withdrawal [1]). Subsequent functional studies can then determine whether individual genes are then active in determining the state change being examined. For example, in a few recent studies, we found a number of genes enriched in proliferating, as compared to differentiating, neural stem/progenitor cell populations [2, 3]. Among the gene candidates identified was MELK, a poorly characterized kinase. We then performed functional studies of MELK in neural progenitors and determined that it is a critical regulator of their self-renewal [4]. Similar to determining the differences between two populations of cells following perturbations, microarrays have been successfully used to understand differences in stem cell populations created by a germline mutation, as in a knockout or transgenic mouse. Molofsky et al. [5], in an elegant manner, used microarrays to discover the molecular mechanisms underlying the effects of the polycomb transcription factor Bmi-1 by analyzing the differences in gene expression between mutant and wild-type neural progenitor cells. Recently, we have analyzed genes upregulated by the elimination of the tumor suppressor gene PTEN in neural progenitors in the hope that we would discover candidates for mediating tumorigenesis [6]. Another way in which array studies can be used to derive information on genes relevant for the function of particular stem cells or universal “stem cell signature” is to analyze those genes that are shared by two or more populations. For example, we and others [7–9] have uncovered sets of genes that are shared by multiple stem cell populations. These genes may then be considered candidates for mediating stem cell-specific processes, such as self-renewal, rather than simply being indicators of the cell or tissue of origin of the cells. Genomic and proteomic studies can also be used to characterize disease states and to delineate markers or sets of markers [10]. For example, proteomic analysis of blood or tissue samples from cancer patients can yield diagnostic and prognostic biomarkers. Microarray analysis of brain tumors, in some cases, provides better prognostic categorization than traditional histologic/pathological categorization. The delineation of cancer- or disease-specific genes or proteins can also yield valuable targets for therapeutic intervention [11]. Large-scale analyses of protein or gene expression may also be the optimal way to define a particular cell type and to compare cells obtained by different investigators under different conditions [12]. Often, the sets of markers used for immunocytochemistry or fluorescence-activated cell sorting (FACS) are insufficient to define a particular cell type. Neural stem cells, for example can be cultured from different central nervous system (CNS) regions under a variety of conditions. These cells all appear to express the intermediate filament nestin and produce neurons, astrocytes, and oligodendrocytes. However, depending on CNS region, stage of development, and culture conditions, neural stem cells may vary greatly in many properties, including self-renewal and differentiation potential [13]. Through the use of genomics or proteomics methods, one can identify fingerprints of the “specific” neural stem cell types, creating unique molecular identifiers facilitating integration of the stem cell data from different laboratories. An emerging area of research within the “-omics” field is an analysis of massive data sets with the goal of the delineation of pathways that regulate specific regulatory processes. By looking at coordinated gene expression and making use of the ever-increasing annotated databases, one can define sets of genes or proteins that are coregulated by a particular manipulation or disease state. In such a way, we have found that the pRB pathway is important for regulating the proliferation of postnatal neural progenitors [1] Properly designed postarray functional studies can reveal the degree to which these sets of genes or proteins interact, determining the key functional regulators of the particular regulatory pathway. The examples delineated above represent only a few of the potential uses for large-scale expression; numerous other uses exist, and still more will be created as methodology becomes readily accessible to investigators without specialized training. Choosing the Appropriate Cells/Tissues To Study The key to success of any profiling study rests in the choice of starting material. Studies range from use of whole tissue to highly purified, sorted cells. Although whole tissue has the advantage of being plentiful, it is generally true that the more pure the starting material, the more specific the profiling data will be. Several approaches can be taken to minimize cellular heterogeneity and maximize purity. The most homogeneous starting material is usually based on clonally expanded cell lines. For those who study neural stem cells, for example, there are several transformed lines available that have many of the properties of neural stem cells. However, these lines do not represent stem cells in their normal state, and caution must be taken in any profiling experiment using them. For some stem cell types, such as hematopoietic stem cells, sufficient numbers of positive and negative extracellular markers exist to allow for FACS-based purification and subsequent study [14]. Neural stem cells, on the other hand, can be enriched by cell sorting using cell surface markers or other methods (such as size or dye exclusion) [15–17], but not to purity. One approach to get at least partway around this heterogeneity is to use promoter-driven green fluorescent protein expression followed by FACS analysis [18]. For example, the nestin promoter, when transduced into freshly dissociated human fetal brain, appears to specifically drive enhanced green fluorescent protein expression in neurosphere-forming multipotent neural progenitors, allowing subsequent purification by FACS and array analysis [18]. Similar methods can be used when green fluorescent protein (GFP) (or another fluorescent protein) expression is driven by a specific promoter in transgenic animals. D'Amour and Gage used the SOX2 promoter to create a transgenic mouse and then performed array analysis on sorted brain-derived neural progenitors, which express SOX2 [19]. One caveat to using this approach is that most promoter fragments that are used to drive reporter gene expression are not 100% faithful to the endogenous gene. That is, expression of the transgene may not entirely mimic expression of the native mRNA or protein. The advent of bacterial artificial chromosome transgenic technology [20], however, should alleviate many of these concerns, since the full gene sequence, or a large part of it, will be used to drive reporter expression. Another problem with using any single gene-based approach is that no marker is absolute. For the more homogeneous population of the progenitors, several rounds of sorting using a combination of a cell surface and GFP markers may be used. Another way to get around the issue of heterogeneity is to perform genetic subtraction between two different populations of cells that differ largely in the number of cells of interest. Several mRNA/cDNA subtraction methods exist, such as representational difference analysis (RDA), polymerase chain reaction (PCR)-based differential display, and comparison of cDNA profiles obtained by serial analysis of gene expression [21–24]. In our previous study, we performed an RDA subtraction on two neurosphere (NS) populations [3]. Neurospheres are derived from neural stem cells, containing a variety of their progeny at different stages of differentiation. Using RDA, we compared mRNA populations of proliferating to differentiating NSs. Proliferating neurospheres contained 10-fold greater numbers of NS-forming neural stem cells. Currently, many microarray platforms are sensitive and broad enough (whole genome size) such that the comparisons between heterogeneous populations can be reasonably made, provided that the two populations mainly differ in the cell types of interest. Except for the expected problems of high-throughput methodologies present in gene expression microarray profiling, the field of proteomics faces additional challenges. One of them is the absolute range of protein expression within one cell or one biological fluid. The protein field has yet to benefit from the protein equivalent to PCR, and thus the success of any proteomics study relies on reducing the complexity of the sample matrix through subcellular fractionation or through depletion of abundant proteins that would otherwise mask low-abundance, yet potentially important, proteins. And although transcript variants can be readily predicted and identified, proteins will often have multiple post-translational modifications, which increases greatly the complexity and diversity of the proteome [25]. The question in proteomics is quickly turning from what proteins are present to what posttranslational modifications are present. Types of Profiling Tools In any type of profiling approach, investigators are faced with a choice of exactly what should be measured. Several approaches exist to measure mRNA expression, whereas others measure protein expression. Recent studies have also begun to perform profiling of small, regulatory RNAs, termed microRNAs [26]. Proteomics Technological and informatics advances have paved the way for the advent of proteomics: the study of large sets of proteins expressed by a particular cell type, tissue, or biological fluid [27]. The use of proteomics complements genomic methods and allows investigators to overcome some drawbacks of genomic approaches [28]. This is an important consideration as more proteins are discovered that are not under classic transcriptional regulation. Recent estimates in hematopoietic stem cells [29] suggest less than 50% agreement between protein and transcript expression levels. Contemporary proteomics has three components: analytical separation to reduce the complexity of the protein matrix, mass spectrometry (MS) analysis of the proteins, and bioinformatic analysis. The most common approach to separation of complex protein mixtures is two-dimensional (2D) gel electrophoresis. Although there are still some drawbacks, advances in the field have greatly improved resolution, reliability, and MS compatibility [30]. Significant improvements specific to the analysis of membrane proteins include immobilized pH gradient gels, which offer extended pH range and steady-state focusing. 2D gel electrophoresis has advanced to the point where protein isoforms can be reliably detected [31] and intact protein mass can be measured [32, 33]. Additional separation techniques, such as two-dimensional liquid chromatography, are also available and offer their own sets of benefits and drawbacks. Recent advances have improved the quantitative nature of protein profiling and include, but are not limited to, isobaric tags [34] and the incorporation of stable isotopes into either living tissue or cell cultures [35]. These separation methodologies are then coupled to various MS platforms [36]. MS is precise, rapid, independent of antibodies, requires nano- to femtomole amounts of protein, generally does not demand 100% protein purity, and is capable of identification of unknown proteins. Using MS, it is possible to analyze the total protein complement, the intact protein mass [37], the amino acid sequence of small peptides (enabling the identification of gene sequence errors), and the nature and location of post-translational modifications [38, 39]. Acquisition of MS data is typically automated, and the interpretation of these data is facilitated by publicly available software and databases, such as Mascot, National Center for Biotechnology Information (NCBI), and SwissProt. Generally, MS analysis programs compare the experimentally determined MS/MS scan of the peptide against all existing peptide sequences from a selected database (such as NCBI or SwissProt), calculate match probabilities, and predict protein identity. The presence of post-translational modifications, such as phosphorylation, can be accommodated and detected within these software programs. De novo sequencing is also possible but can be experimentally challenging. Genomics Simultaneous measurements of messenger RNAs encoding a large number of genes can be accomplished in a number of ways, starting with simple subtraction techniques such as RDA. The most common method used is microarray analysis, which is discussed in detail below. Alternatives that are more or less comprehensive to microarrays also exist. Multiplex quantitative reverse transcription (RT)-PCR can be used to assay expression levels of tens of genes. This method allows for a more precise level of quantitation and does not require the user to have access to an array analysis facility. Thus, multiplex quantitative PCR could be of significant use when one wants to study a limited number of genes [40, 41]. Recent advances in multiplexing, based on specific oligonucleotides tagged with beads or signature molecules or particular mass, will make the screening of a limited number of genes in a large number of samples highly efficient [42–44]. On the other hand, methods exist that give an even broader picture of gene expression. The massively parallel signature sequencing method uses a proprietary technology to determine the number of each transcript and is purported to have a much higher sensitivity than microarray for low copy number transcripts. This method has been successfully used to delineate global gene expression in embryonic stem cells and compare them to differentiating cells [45–50]. With the introduction of cDNA and oligonucleotide microarrays in the mid-1990s [51–53], high-throughput, simultaneous monitoring of gene expression became possible. DNA microarrays consist of a group of methods that allow the instantaneous study of the expression patterns of thousands of genes in the same tissue or cell in parallel [54]. cDNA and oligonucleotide arrays have been used successfully in studying the nervous system, in health (e.g., [1, 3, 55, 56]) and disease [57–62]. Microarray Experimental Flow The typical procedures involved in a microarray experiment include isolation of a messenger or total RNA from the tissue or cell culture sample; labeling of the sample with fluorophores (e.g., Cy3-dCTP or Cy5-dCTP), often in conjunction with amplification; hybridization of samples onto array (slide); raw data acquisition; and subsequent analysis. Postarray steps include data interpretation that typically results in “hypothesis generation” and its independent confirmation or “hypothesis testing.” Here, we will briefly discuss the use of microarray technology in the stem cell research, reliable commercial microarray platforms, available resources for microarray data analysis and interpretation, and importance of “postarray” studies as a standard of microarray-based research. The most common microarray experiment has traditionally used the two-channel design. In this scenario, two samples are labeled with different fluorophores that emit different wavelengths and can be independently quantified. Both samples are hybridized onto the same slide, and the signals from the two samples can be directly compared. Differences in gene expression are then given as a ratio rather than as an absolute value. Such a system was routinely used with all custom and commercial cDNA microarrays, as well as with some commercial oligonucleotide platforms (e.g., Agilent Technologies, Palo Alto, CA, http://www.agilent.com). Over the last 5 years and with the introduction of novel methods of array printing, oligonucleotide-based platforms became more popular because of their advantages in reproducibility and sensitivity over cDNA-based platforms. As a rule, commercial oligonucleotide platforms tend to use a single-channel design, allowing more flexibility in experimental comparisons. In a single-channel experiment, all samples are labeled with one dye, and only one sample is hybridized onto the slide. The signal detected for each probe upon laser excitation is directly proportional to the amount of labeled target bound to it, thus allowing for semiquantitative analysis of the transcript abundance in a given sample. The samples (slides) can be compared between each other, and the differences in the expression are identified. Examples of such platforms are Affymetrix GeneChips (Santa Clara, CA, http://www.affymetrix.com), CodeLink Expression Bioarrays (GE Healthcare, Little Chalfont, Buckinghamshire, U.K., http://www.gehealthcare.com/) and Illumina BeadChips (Illumina, San Diego, http://www.illumina.com). Agilent arrays can also be used for one-channel experiments because of their high level of reproducibility from slide to slide [55]. Depending on the labeling technique and source of experimental RNA, an entire microarray experiment from RNA isolation to raw data acquisition might take up to 3 days, especially when working with finite amounts of starting materials, such as laser-captured or FACS-collected cells [55]. Due to the large scale of a microarray experiment, there are a number of procedural considerations in almost every step of the experimental flow. One of the restricting factors in applying microarray technology in a laboratory is the cost of replicating experiments. To produce reliable results, replication of experiments is prerequisite for reduction of biological and experimental noise [63, 64]. The number of replicates for an experiment will vary depending on the amount of experimental noise; however, replicates introduce greater reliability to the expression data and should not be neglected. When using T7-based labeling technique and high-quality arrays, it is standard to run from three to five independent replicates, each duplicated with switched dyes (in case of two-channel design), to obtain a low enough number of false-positive signals [65]. Noise measures can be empirically derived and screening thresholds set appropriately based upon the technical and biologic noise in a particular system [56, 63, 64]. Increasing the number of independent replicates will permit the detection of smaller changes in expression (e.g., 1.5-fold) with higher confidence (e.g., [55]). Statistical methods that estimate variance to increase statistical power are very useful when small numbers of replicates are available compared with the number of measurements being made [66, 67]; several excellent reviews of statistical methods are available [67–70], and tools are available on line (as described in Online Resources). Whether or not to pool samples is another question that is often raised. Pooling is an effective way to diminish the effects of individual variability within biological samples. But the power of this approach depends upon the integrity of the samples being pooled, and one sample with significant deviation from the rest of the pool may spoil an experiment consisting of a comparison of two pools. Sample Amplification and Labeling The purity and quality of the starting RNA has a major effect on the results of microarray experiments; therefore, it is essential that all steps of RNA isolation be carried out with maximum care and speed. The major limiting factor in cell-specific gene expression experiments is an ability to reliably amplify and label finite amounts of starting RNA, avoiding introduction of amplification bias [54]. The signal intensity from hybridization depends on the target concentration, the amount of immobilized probe molecules, and the method of labeling. Today, the most common method used with commercial microarray platforms is a T7-directed in vitro transcription and amplification [65]. It was shown to be reliable in generating labeled products from small quantities of RNA on a consistent basis, in some cases from a single cell or a few laser-captured or FACS-collected cells [55, 71]. Commercial Microarray Platforms A typical DNA microarray consists of tens of thousands of elements, called probes, densely deposited onto a solid surface, such as glass, beads, or a membrane. The probes comprise either cDNA sequences [53] or short synthetic oligonucleotides of up to 70 nucleotides (Affymetrix) [72–74]. Microarray platforms can therefore be divided into two major formats, oligonucleotide arrays and cDNA arrays [51, 75, 76]. Due to the increasing popularity of commercial (typically oligonucleotide-based) microarray slides, we will discuss several of the most commonly used commercial platforms. Today, practically all available commercial platforms cover nearly the entire genome, containing up to 50,000 genes on one slide [77], while also offering specific arrays with customer-selected probe content for more detailed and focused gene expression studies. It should be noted that availability of so-called “whole genome” arrays is a bit misleading, as these arrays operate with the number of genes on the array, not the number of actual transcripts detected. Most of the genes in the genome generate multiple transcripts, often with different functions, and are expressed in a specific tissue at a particular time of development. Currently, there is no good estimate of how many transcripts human transcriptome possesses, but it is most likely at least 5–10 times more then the number of genes in the genome, bringing us to nearly 400,000 different transcripts. Therefore, when working with whole-genome microarrays, one should be aware that at best one has in one's possession 40,000 different probes, where a specific gene probe can often recognize either one or multiple transcripts of the same gene. Therefore, it is difficult to estimate what part of transcriptome a particular whole-genome microarray represents. Oligonucleotide microarrays can be manufactured either using in situ synthesis by photolithography (e.g., Affymetrix) or deposition of already synthesized oligonucleotides (e.g., ink jet technology, Agilent; Illumina). Some of the strategies for probe selection are common to all oligonucleotide arrays. Melting temperature of an oligonucleotide probe is calculated based on experimentally derived computer models calculating hybridization behavior of target sequences in complex mixtures under particular conditions. Commercial platforms are summarized in Table 1. Table 1. Commercial microarray platforms Open in new tabDownload slide Open in new tabDownload slide Open in new tabDownload slide Open in new tabDownload slide Open in new tab Table 1. Commercial microarray platforms Open in new tabDownload slide Open in new tabDownload slide Open in new tabDownload slide Open in new tabDownload slide Open in new tab Affymetrix The GeneChip (Affymetrix) arrays are the most widely used of the commercial platforms. They are manufactured using a combination of photolithography and combinatorial chemistry [74]. This allows the synthesis of hundreds of thousands of different oligonucleotides on the same surface at an extremely high density. Because the resulting surface area is very small, it enables researchers to use small sample volumes, therefore reducing the amounts of starting RNA. Affymetrix offers a range of preprinted arrays covering up to 54,000 genes. Each transcript is represented by 11–16 short 25-mer oligonucleotides selected according to their specificity to the desired transcript and low cross-hybridization with similar but unrelated sequences. Because probes are designed for significantly unique regions of genes even among gene family members, GeneChip arrays can distinguish transcripts that are up to 90% identical. In addition, some probes are designed to distinguish multiple splice or polyadenylation variants (Table 1). Agilent An industrial noncontact inkjet printing process is used for the manufacturing of Agilent microarrays. Both oligonucleotide and cDNA can be deposited. The reproducible deposition of oligonucleotide or cDNA molecules onto specially treated glass slides is achieved without actual contact with a surface, thereby reducing the risk of potential anomalies due to the physical contact of slide and printer surfaces (http://www.chem.agilent.com). The technology requires only picoliters of DNA per spot. The 60-mer oligonucleotides are synthesized using standard phosphoramidite chemistry. Microarrays covering up to 50,000 genes per slide are available. This platform has proved to be very sensitive and reliable, and all types of experiments, including ones using FACS cells, have been performed successfully [55]. GE CodeLink CodeLink Activated Slides (General Electric Healthcare) are specially treated to covalently immobilize amine-modified DNA. The combination of cross-linked polymer and endpoint attachment allows the oligonucleotides to be more accessible to the labeled targets hybridized onto the slides (http://www1.amershambiosciences.com). Whole Genome Bioarrays are available containing functionally validated, specific, prescreened 30-mer probes. As is the case for the other cDNA and oligonucleotide arrays described here, publications support the sensitivity, reproducibility, and validity of the data obtained with this platform [79–81]. Illumina BeadChips Illumina BeadChips are another oligonucleotide-based platform that uses 50-mer probes. The unique feature of this platform is the ability to process multiple samples (currently up to six for the whole genome screen) on a single slide, greatly increasing specificity and reducing cross-array variability. Illumina BeadArray technology uses gene-specific probe sequences concatenated with “address” sequences, which are immobilized to a bead along with hundreds of thousands of probes of the same sequence [82]. BeadChip arrays provide extensive genomic coverage for well-annotated genomes such as human or mouse. The labeling protocol uses a T7 amplification technique that has been optimized for single-round amplification of as little as 50 ng of total RNA. As with other vendors, Illumina offers custom probe content for more focused multiple experiments. Data Analysis and Interpretation Currently, there is no standard or consensus on the best way to represent or analyze microarray data. This is a rapidly changing field, and methods are continuously evolving. However, there are several general data analysis and interpretation steps that are requisite for most microarray experiments. In addition, consensus has been reached about how microarray data should be presented, shared, and annotated in the minimal information about a microarray experiment (MIAME; http://www.mged.org). In general, microarray data are normalized, and the relative expression of each gene within a sample is determined. Following initial assignment of expression values, the data can be subject to a wide variety of analyses. As stated in the section above, statistical analyses are used to determine which genes are enriched or reduced in one experimental condition or another. In this way, individual genes of interest can be identified. Before individual signal intensity values are compared, normalization is necessary. This critical step compensates for technical variability that includes inconsistency between slides, different rates of fluorescent dye incorporation (e.g., Cy3 is generally incorporated more efficiently them Cy5), and other systematic sources of error. Normalization adjusts measured signal intensities appropriately. Raw data filtering is also performed by removing poor or questionable spots (signals). Several types of data normalization are used, and depending on a particular experiment and the microarray platform used, a particular type (often suggested and developed by a vendor) should be used. In this regards, we have had success with the Microarray Data Analysis system of The Institute for Genomic Research (TIGR) that combines several analytical and data conversion software in one suite (http://www.tm4.org/). It provides users with an intuitive interface to design data analysis flow, array normalization, and gene identification tools. After normalization and filtering, expression values can be analyzed and compared between experimental samples. Many sophisticated algorithms for microarray data clustering, visualization, classification, statistical analysis, and biological theme discovery have been developed (e.g., http://www.tm4.org/). At this final stage of data analysis, the use of a variety of analytic techniques is critical, as every algorithm or statistical method has strengths and weaknesses. Therefore, several analytical strategies should be exploited to generate a reliable set of candidate genes affected in the experiment. Frequently used analytical tools include direct statistical interrogation for significantly deregulated genes (e.g., t tests or analysis of variance) and various types of data clustering, where groups of genes with similar behavior across experimental conditions can be identified. The latter include such clustering algorithms as hierarchical clustering [83], self-organizing maps, K-means clustering [84], and principal components analysis [85]. Using the TM4 software suite allows researchers to label and track identified gene clusters through other analyses, giving the ability to compare expression behavior from experiment to experiment. A global sense of the similarities or differences between sample sets can be obtained using clustering algorithms. For example, using hierarchical clustering, we performed an analysis of the expression profile specific for PTEN-deficient neurospheres and confirmed its specificity [6]. This cluster analysis can be extended to include hundreds of genes in hundreds of individual samples and can become a powerful tool in the grouping of samples. For example, Freije et al. have used cluster analyses to group glioblastoma multiforme samples into novel categories with prognostic significance [86]. In addition, as described above, gene expression data can be mined to examine functional groupings of differentially expressed genes. Analysis tools, such as DAVID/Ease (http://david.niaid.nih.gov/david/ease.htm), allow one to determine whether a tissue or cell type differentially expresses particular kinds of genes or genes involved in specific processes. As more functional data are amassed about individual genes, the annotation becomes more and more detailed and, hence, more sophisticated. Numerous other methods and resources exist for the analysis of array data. Some of these resources are listed at the end of this review. Microarray Data Confirmation: “Postarray” Studies Even with good statistical methods, confirmation of some small cross-section of the results using an alternative method, such as quantitative RT-PCR, in situ hybridization, or Northern blot, is necessary. Quantitative and real-time RT-PCR are both especially suitable in validating a large number of gene expressions (e.g., [1, 55]); however, in situ hybridization experiments can also be performed to confirm the expression of dozens of genes within a relatively short period of time and provide in vivo validation of gene expression data [2, 3]. Northern blots also offer a way to confirm the expression of transcripts and have demonstrated the consistency and validity of cDNA microarray data [80]. Microarrays—Conclusions The field is changing rapidly, and new techniques and platforms are introduced every year. Commercial oligonucleotide platforms are becoming standards in the field. Whole genome chip sets are now available, and the general cost of microarray experiments is decreasing. Simple and reliable amplification and labeling techniques are still needed as more and more researchers are looking at the cell-specific expression levels. There are a variety of analytic tools, and certain standards are starting to appear. Data mining and interpretation is still a challenge, as complete information on gene function is not always available. Functional confirmation becomes a standard and a part of microarray experimental design; this is one of the most exciting areas of progress. Internet resources are rapidly changing, so we refer readers to some general array websites that should keep them up-to-date. Online Resources General Microarray Sites Microarray Gene Expression Database group: http://www.mged.org TIGR: http://www.tigr.org/tdb/microarray Stanford University: http://cmgm.stanford.edu/pbrown/protocols/index.html DeRisi Laboratory, University of California San Francisco: http://derisilab.ucsf.edu/ Y.F. Leung's Functional Genomics: http://ihome.cuhk.edu.hk/%7Eb400559/array.html Data Collection, Annotation, and Interpretation Tools PubMed: http://www.ncbi.nlm.nih.gov/PubMed Database for Annotation, Visualization and Integrated Discovery (DAVID): http://david.abcc.ncifcrf.gov PubGene, University of Oslo: http://www.pubgene.org/ SOURCE, Stanford University: http://source.stanford.edu GenMapp, Gene Microarray Pathway Profiler: http://www.genmapp.org/ 2HAPI, High-density Array Pattern Interpreter, version 2, University of California San Diego: http://david.abcc.ncifcrf.gov ChiliBot: http://www.chilibot.net/ Genomatix microarray data interpretation tools: http://www.genomatix.de/ MeSHer biological literature mining: http://biocomp.dfci.harvard.edu/mesher.html MatchMiner translates between different gene identifications: http://discover.nci.nih.gov/matchminer/index.jsp Data Analysis Tools TM4 microarray analysis software, TIGR: http://www.tigr.org/softlab Cyber-T, Institute for Genomics and Bioinformatics, University of California: http://visitor.ics.uci.edu/genex/cybert/ EMBL, European Bioinformatics Institute: http://ep.ebi.ac.uk Patterns from Gene Expression (PaGE), University of Pennsylvania: http://www.cbil.upenn.edu/PaGE SCAN-ALYZE, Lawrence Berkeley National Laboratory: http://www.microarrays.org/software.html Rosetta Resolver System: http://www.rosettabio.com/products/resolver/default.htm ImaGene and GeneSight, BioDiscovery, Inc.: http://www.biodiscovery.com GeneSpring, Silicon Genetics, Inc.: http://www.sigenetics.com Spotfire DecisionSite for Functional Genomics, Spotfire, Inc.: http://www.spotfire.com Metabolic and Regulatory Pathway Databases Kyoto Encyclopedia of Genes and Genomes (KEGG): http://www.genome.ad.jp/kegg/ TRANSPATH database describes the signal transduction from the ligand at the surface of a cell up to the transcription factor: http://www.gene-regulation.com/cgi-bin/pub/databases/transpath The Signaling PAthway Database (SPAD): http://www.grt.kyushu-u.ac.jp/spad/ Wnt signaling pathway database: http://www.stanford.edu/∼rnusse/wntwindow.html Boehringer Mannheim Biochemical Pathways: http://www.expasy.org/cgi-bin/search-biochem-index Enzymes and Metabolic Pathways (EMP) database: http://www.empproject.com BioCarta: http://www.biocarta.com Disclosure of Potential Conflicts of Interest The authors indicate no potential conflicts of interest. Gene Therapy for Hemoglobinopathies Using Autologous Hematopoietic Stem Cells Punam Malik Saban Research Institute, Division of Hematology-Oncology, Children's Hospital Los Angeles, Department of Pediatrics and Pathology, University of Southern California Keck School of Medicine, Los Angeles, California, USA Key Words. Thalassemia • Hemoglobin • Retroviral vectors • Lentiviral vectors ABSTRACT Hemoglobin disorders constitute the most common single-gene disorders that are potentially amenable to gene therapy. However, retroviral vectors carrying the human β-globin cassette are notoriously unstable, express the transgene at low levels, or are unable to hold large erythroid regulatory elements. In the past 5 years, tremendous progress has been made in this field with the use of lentiviral vectors. Our laboratory investigated lentiviral vectors for erythroid lineage-specific expression, long-term expression, and silencing following transduction of hematopoietic stem cells. In addition, we have been able to overcome the chromatin position effects with insulated self-inactivating lentiviral vectors that have increased probability of expression from individual integrants and reduced clonal variegation in expression in long-term transplanted mice. We have shown complete correction of the human thalassemia phenotype in vitro and in xenografts in the red blood cell progeny of CD34+ cells from patients with β-thalassemia major. This article provides a concise review of the current status of gene therapy for hemoglobin disorders and the steps needed for safe human clinical trials. Introduction Expressing a normal β-globin gene or an antisickling globin in red blood cells following permanent gene transfer into hematopoietic stem cells (HSCs) can result in permanent cure for hemoglobinopathies, such as β-thalassemia and sickle cell anemia. Therefore, retroviral vectors, which integrate permanently into host genome, have been explored for gene transfer into HSCs. Gene therapy for hemoglobinopathies, however, suffered from several obstacles: the commonly used onco-retroviral vectors carrying the β-globin gene and its regulatory elements have notoriously suffered from problems of vector instability, low titers, and variable expression of the human β-globin gene [1–6]. AAV vectors, which initially held promise, did not integrate efficiently into HSCs [7] and were restricted to carrying only the core elements of the large β-globin locus control region (LCR) due to their size limitation [8–10]. RNA-based approaches, such as use of hammerhead and trans-splicing ribozymes [11, 12], antisense RNA against βS globin [13, 14], or DNA short-fragment homologous recombination [15] to convert βS to β-globin have also been tried and show promise, but they require improvements in methods of gene delivery and demonstration of efficacy in animal models. For detailed recent reviews on gene therapy for hemoglobinopathies, the reader is referred to [16–20]. Gene Therapy for β-Thalassemia Lentiviral vectors have the distinct advantage of integrating into nondividing cells, such as the hematopoietic stem cells. We designed several erythroid human immunodeficiency virus 1 (HIV-1)-based lineage-specific lentiviral vectors [21] in a self-inactivating (SIN) vector backbone, whereby the 3′ long terminal repeat (LTR) enhancer deletion gets copied over into the 5′ LTR, removing viral enhancer elements in the integrated virus. These SIN-lentiviral vectors expressed in a highly lineage-specific manner [21] and resulted in correction of murine protoporphyria [22]. However, SIN lenti-provirus was subject to chromosomal position effects [23]. In a pioneering study performed by May et al., HIV-1-based lentiviral vectors were shown to stably transmit the human β-globin gene and large elements of the LCR, resulting in the correction of β-thalassemia intermedia phenotype in mice [24]. Several other groups subsequently showed correction of β-thalassemia intermedia phenotype in mice, using either β− or γ-globin lentiviral vectors. These results are summarized in Table 2. Table 2. Summary of studies on lentiviral vectors for gene therapy for hemoglobin diseases Open in new tabDownload slide Open in new tabDownload slide Open in new tabDownload slide Open in new tabDownload slide Open in new tab Table 2. Summary of studies on lentiviral vectors for gene therapy for hemoglobin diseases Open in new tabDownload slide Open in new tabDownload slide Open in new tabDownload slide Open in new tabDownload slide Open in new tab Homozygous β-thalassemia (thalassemia major) is embryonic-lethal in mice, since the globin switch occurs in utero. Rivella et al. [25] developed a mouse model of β0-thalassemia major by transplanting fetal liver cells from thalassemia major fetuses, prior to fatality, into lethally irradiated normal adult mice. Fully engrafted mice died of severe anemia (hemoglobin ∼3 g/dl) within 6–8 weeks following the transplantation, whereas genetic correction of the fetal liver cells with a lentiviral vector, previously shown to correct thalassemia intermedia phenotype in mice [24], rescued their lethality. However, it is to be noted that most mice were still severely anemic, with hemoglobin ranging from 4.7–7.5 g/dl (a severe thalassemia intermedia phenotype), whereas one mouse showed complete correction (hemoglobin [Hb] 12 g/dl). Similar data on variable expression of β/γ globin have emerged from other laboratories, where despite therapeutic levels of globin gene expression, there was marked variability in transgene expression among different mice [25–27]. Persons et al. showed that the γ-globin/LCR vector expression did not correlate with vector copy number, and this was due to chromosomal position effects [26]. The same laboratory has shown that the same vector, TNS9, resulted in an average of 3.8 g/dl increase in hemoglobin/vector copy number in thalassemia intermedia mice in one study [24] versus a 2.3 g/dl increase in hemoglobin/vector copy number in the thalassemia major mouse model in another study [26]. Imren et al. showed panerythroid correction of murine β-thalassemia intermedia only with 3–5 copies per cell, whereas minimal correction occurred at single vector copy per cell in the mice [27]. They attributed this to chromosomal position effects [27]. Our laboratory designed a β-globin gene/LCR containing lentiviral vector carrying the chicken hypersensitive site 4 (cHS4) chromatin insulator element, such that it flanks the provirus upon integration, to address chromatin position effects and, in addition, improve vector biosafety. This vector was used to transduce CD34+ cells from bone marrow of four patients with transfusion-dependent thalassemia major [28]. There was high-level transduction with complete correction of the in vitro model of human thalassemia erythropoiesis, and this correction was sustained long-term in vivo in immune-deficient mice. Gene Therapy for Sickle Cell Anemia Lentiviral vectors have also paved the way for gene therapy for sickle cell anemia (SCA). At a molecular level, sickling occurs when sickle hemoglobin (Hb S) pairs between the mutant valine-6 in the β2 chain of one hemoglobin molecule and a hydrophobic pocket, formed by phenylalanine-85 and leucine-88 in the β1 chain of another hemoglobin molecule (α2βS2). Gamma globin is a natural antisickling hemoglobin, because glutamine-87 of γ-globin aligns with threonine-87 of βS-globin, resulting in mixed tetramers (α2γβS) that do not participate in polymer formation. Several synthetic antisickling β-globins have been designed based on similar principles [29–31] and are an attractive approach to gene therapy for SCA. Pawliuk et al. [32] were the first to express an antisickling β-globin (β-T87Q-globin) from a lentivirus, with correction of phenotype in two murine models of SCA: (a) the SAD [33] mice that express a “super-sickling” βSAD globin (with Sickle, Antilles, and hemoglobin D-Punjab mutations in human β-globin), and (b) the BERK mice [33] that express human α and human βS globins and, in addition, are knockouts for murine α and β globins. Ryan et al. had also generated transgenic sickle mice at the same time the BERK mice were generated [34] that exclusively produced human α and human βS globins. The same group used a self-inactivating lentiviral vector carrying antisickling globin (β-AS3) to correct the disease in these transgenic sickle mice [35]. Notable features of this study were very high gene transfer efficiency into hematopoietic stem cells and a very short cytokine-free exposure in primary and secondary recipients. Recently, Samakoglu et al. have used short hairpin RNA in lentiviral vectors to destroy the βS globin and introduce the antisickling γ-globin gene [36]. All of these studies are tremendous strides toward gene therapy for SCA. Safety of Integrating Vectors The development of leukemia in three children treated with a γ-oncoretroviral vector carrying the IL2R-γ chain and oncogene activation by the integrated proviral LTR [37] underscore the need for generating safer vectors. Lentiviral vectors, like oncoretrovirus vectors, preferentially integrate near or around cellular genes [38]. Although γ-oncoretroviral vectors have now been shown to prefer integration start sites of actively transcribed genes, lentiviral vectors integrate within active genes, with no predilection for promoters and integration start sites [38, 39]. Imren et al. showed preferred intragenic integration of the β-T87Q-globin/LCR lentiviral vector in human cord blood progenitor cells that were transplanted into immune-deficient mice, with several integrations occurring near oncogenes [40]. These studies emphasize the need for safe vector design. The latter is especially important, given that three children cured of X-linked severe combined immunodeficiency in the French trial went on to develop a T-cell leukemia, which was found to be due to activation of surrounding LMO2 oncogene by the provirus. Chromatin insulator elements, such as those from cHS4, have been shown to have chromatin barrier activity and an enhancer-blocking effect, two separable activities [41, 42]. Although the barrier function of the chromatin insulator elements has been tested in γ-oncoretroviral vectors [43–45] and in SIN-lentiviral vectors [46, 47] by several groups, the enhancer-blocking activity of the insulators in the context of viral vectors has not been studied. Chromatin insulator elements should be tested in the context of β− or γ-globin lentiviral vectors, since these vectors carry the LCR, a strong erythroid enhancer, and the LCR's propensity to activate erythroid genes surrounding the integrated provirus needs to be determined. In its native configuration, the β-globin LCR can activate erythroid genes over large distances. We have shown improved barrier activity by incorporating cHS4 to flank the β-globin SIN-LV cassette and preliminary reports now suggest that the cHS4 insulator may reduce vector genotoxicity in assays designed to test oncogenicity of vectors (Malik P. et al. and Neinhuis A. et al., unpublished results). It is to be noted, however, that although insulator elements improve safety by blocking enhancers from “oncogene-activating” insertions, can conceivably block cellular enhancers from activating tumor-suppressor genes. Therefore, the effects of insulator elements on transgene expression, silencing, and enhancer blocking need to be studied in depth. Other groups have adopted a different approach to address safety, such as homologous recombination using AAV vectors or zinc-finger nucleases, to correct the specific mutation [48–50]. Although they are in their infancy, these targeted approaches may eventually be safer than the relatively randomly integrating viral vectors. In summary, studies on lentiviral vectors for gene therapy for hemoglobin disorders, although experimental, have paved the way for preclinical studies on gene therapy for β-thalassemia and sickle cell anemia, so that safety and feasibility clinical trials can ensue. 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TI - Report on the Workshop “New Technologies in Stem Cell Research,” Society for Pediatric Research, San Francisco, California, April 29, 2006
JF - Stem Cells
DO - 10.1634/stemcells.2006-0397
DA - 2007-04-01
UR - https://www.deepdyve.com/lp/oxford-university-press/report-on-the-workshop-new-technologies-in-stem-cell-research-society-edHQfbJzWl
SP - 1070
EP - 1088
VL - 25
IS - 4
DP - DeepDyve
ER -