Posts Tagged ‘iPSCs’

Writer and curator: Larry H. Bernstein, MD, FCAP and
Curator: Aviva Lev-Ari, PhD, RN

There is an explosion of work-in-progress in applications to regenerative medicine using inducible pluripotent stem cells in both endothelial and cardiomyocyte postischemic repair, and also in post bone marrow radiation restoration, with benefits and hazards.  The following article is quite novel in that it deals with stem cell regulation by DNA methylation.  Therefore, it deals with the essentiality of methylation of DNA in epigenetic regulation.

This is the fourth discussion of a several part series leading from the genome, to protein synthesis (1), posttranslational modification of proteins (2), examples of protein effects on metabolism and signaling pathways (3), and leading to disruption of signaling pathways in disease (4), and effects leading to mutagenesis.

1.  A Primer on DNAand DNA Replication

2.  Overview of translational medicine

3.  Genes, proteomes, and their interaction

4. Regulation of somatic stem cell Function

5.  Proteomics – The Pathway to Understanding and Decision-making in Medicine

6.  Genomics, Proteomics and standards

7.  Long Non-coding RNAs Can Encode Proteins After All

8.  Proteins and cellular adaptation to stress

9.  Loss of normal growth regulation


Posttranslational modification is a step in protein biosynthesis. Proteins are created by ribosomes translating mRNA into polypeptide chains. These polypeptide chains undergo
PTM before becoming the mature protein product.

Regulation of somatic stem cell Function by DNA Methylation and Genomic Imprinting

Mo Li1, Na Young Kim1, Shigeo Masuda1 and Juan Carlos izpisua Belmonte1,2 1Salk institute for Biological Studies, 10010 N Torrey Pines Rd, La Jolla, CA 92037, USA. 2Center of Regenerative Medicine in Barcelona, Dr Aiguader, 88, 08003 Barcelona, Spain. Corresponding author email:

Cell & Tissue Transplantation & Therapy 2013:5 19–23
This article is available from


Epigenetic regulation is essential for self-renewal and differentiation of somatic stem cells, including

  • hematopoietic stem cells (HSCs) and
  • neural stem cells (NSCs).

The role of DNA methylation, a key epigenetic pathway,

  • in regulating somatic stem cell function
    • under physiological conditions and during aging

has been intensively investigated.

Accumulating evidence highlights the dynamic nature of

  • the DNAmethylome
    • during lineage commitment of somatic stem cells and
  • the pivotal role of DNAmethyltransferases in
    • stem cell self-renewal and differentiation.

Recent studies on genomic imprinting have shed light on

  • the imprinted gene network (IGN) in somatic stem cells,
  1. where a subset of imprinted genes remain expressed and
  2. are important for maintaining self-renewal of these cells.

Together with emerging technologies, elucidation of the epigenetic mechanisms regulating somatic stem cells with normal or pathological functions may contribute to the development of regenerative medicine.

Keywords: somatic stem cells, epigenetics, DNA methylation, genomic imprinting, hematopoietic stem cells, neural stem cells


In adult animals, somatic stem cells (also known as adult stem cells) are responsible for maintaining tissue homeostasis and participate in tissue regeneration under injury conditions. Self-renewal and differentiation are two important aspects of somatic stem cell function. Epigenetic mechanisms underlying these processes have been intensively investigated. With the increasing ability

  • to identify and manipulate somatic stem cell populations from diverse tissues,
  • it is possible to dissect the epigenetic pathways that are
  1. either unique for a specific tissue or
  2. universally important in regulating stemness and differentiation.

Epigenetic control of somatic stem cell function exists at various levels, including

  • DNA methylation,
  • histone modification, and
  • higher-order chromatin structure dynamics.

Here, we focus on recent progress in our understanding of how

  • DNA methylation regulates somatic stem cell function.

DNA Methylation and stem cell Function

The role of DNA methylation in somatic stem cell compartments has gained increasing attention. Recent  evidence has shown that

  • DNA methylation is dynamically regulated during somatic stem cell differentiation and aging.1

A study of methylomes of human hematopoietic stem cells (HSCs) and two mature hematopoietic lineages,

  • including B cells and neutrophils, showed that
    • hypomethylated regions of lineage-specific genes often become methylated in opposing lineages, and that
    • progenitors display an intermediate methylation pattern

that is poised for lineage-specific resolution.2

Another study compared genome-wide promoter DNA methylation in human cord blood hematopoietic progenitor cells (HPCs) with

  • that in mobilized peripheral blood HPCs from aged individuals.

It was found that aged HPCs lose DNA methylation in a subset of genes that are hypomethylated in differentiated myeloid cells and

  • gain de novo DNA methylation at polycomb repressive complex 2 (PRC2) target sites.3

It was hypothesized that such epigenetic changes contribute to age-related loss of HSC function, such as a bias toward myeloid lineages. Recently, Beerman et al. studied the global DNA methylation landscape of HSCs in the context of

  • age-associated decline of HSC function.4

Over- all, the DNA methylation landscape remains stable during HSC ontogeny. However, HSCs isolated from old mice display higher global DNA methylation. Interestingly, they observed

  • localized DNA methylation changes in genomic regions associated with hematopoietic lineage differentiation.

These methylation changes preferentially map to genes

  • that are expressed in downstream progenitor and effector cells.

For example, genes that are important for the lymphoid and erythroid lineages

  • become methylated in “old” HSCs,

which is consistent with

  • the decline of lymphopoiesis and erythropoiesis during aging.

Additionally, inducing HSC proliferation by 5-fluorouracil treatment or

  • by limiting the number of transplantedHSCs
    • recapitulates the functional decline and DNA methylation changes during physiological aging.

A closer examination of the overlapping genes with significant DNA methylation changes during aging or enforced proliferation showed

  • an enrichment of DNA hypermethylation at PRC2 target loci,

echoing the observation by Bocker et al. in human HSCs.

Interestingly, a recent report showed that epigenetic alterations such as DNA hypermethylation that are accrued during aging,

  • can be fully reset by somatic reprogramming,

raising an interesting possibility that these aging-related epigenetic defects may be reserved by small molecules.5

Methylation of cytosines at CpG dinucleotides is catalyzed by three key enzymes.

DNA (cytosine-5)- methyltransferase 1 (DNMT1) is responsible for maintaining DNA methylation patterns during DNA replication

  • by methylating the newly synthesized hemi-methylated DNA.

The other two DNA methyltransferases, DNMT3a and DNMT3b,

  • are not DNA replication-dependent and can methylate fully unmethylated DNA de novo.

They are responsible for establishing new DNA methylation patterns during development.

DNMT3a, a gene required for neurogenesis,

  • is expressed in postnatal neural stem cells (NSCs).

In NSCs, DNMT3a methylates non-proximal promoter regions, such as gene bodies and intergenic regions. Surprisingly, rather than silencing gene expression,

DNMT3a-mediated DNA methylation in gene bodies antagonizes Polycomb-dependent repression and

  • facilitates the expression of neurogenic genes.6

The role of DNMT3a in HSCs has also been investigated. Both Dnmt3a and Dnmt3b are expressed in HSCs. An earlier study did not identify any defects in HSC function when Dnmt3a or Dnmt3b was removed.  However,

  • HSCs lackingboth of these de novomethyltransferases
    • fail to self-renew, yet retain the capacity to differentiate.7

A more recent study re-examined

  • the consequences of Dnmt3a loss in HSCs and
  • uncovered a progressive defect in differentiation that is only manifested during serial transplantation.8

At the molecular level, while Dnmt3a loss results in the expected hypomethylation at some loci,

  • it counterintuitively causes hypermethylation in even more regions.8

This seemingly paradoxical result echoes the  unconventional role of Dnmt3a in transcriptional  activation in NSCs (as discussed above). Both cases suggest a more complex regulatory function of DNMT3a that is

  • beyond simply methylating DNA.

In contrast, the loss of Dnmt1 produces more dramatic and immediate phenotypes in HSCs, manifested

  • in premature HSC exhaustion and
  • block of lymphoid differentiation,

highlighting the distinct requirements for different DNA methyltransferases in HSCs.9,10

Genomic Imprinting and stemness

DNA methylation also underlies genomic imprinting, which is an

  • evolutionarily conserved epigenetic mechanism of ensuring appropriate gene dosage during development.

One allele of the imprinted genes is

  • epigenetically marked by DNA methylation to be silenced according to the parental origin.

The pattern of imprinting

  • is established in germ cells and maintained in somatic cells.

Imprinted genes are thought to play critical roles in organismal growth and are relatively downregulated after birth.11 Recently, a series of reports demonstrated that

  • a subset of imprinted genes belonging to the purported imprinted gene network (IGN)12
  • remain expressed in somatic stem cells and
  • are important for maintaining self-renewal of these cells.

Through gene expression profiling, one group identified that several members of the IGN are expressed in

  1. murine muscle,
  2. epidermal, and
  3. long-term hematopoietic stem cells
  4. as well as in human epidermal and hematopoietic stem cells.13

In particular, the paternally expressed gene 3 (Peg3) gene was shown by another group

  • to mark cycling and quiescent stem cells in a wide variety of mouse tissues.14

The role of imprinted genes in regulating somatic stem cell function has been examined in two types of tissues.

In bronchioalveolar stem cells (BASCs), a lung epithelial stem cell population,

  • expression of IGN members is required for their self-renewal.

Bmi1, a polycomb repressive  complex 1 (PRC1) subunit,

  • is essential for controlling the expression of imprinted genes in BASCs without affecting their imprinting status.15

In Bmi1 mutant BASCs,  many members of the IGN become derepressed,

  • including p57, H19, Dlk1, Peg3, Ndn, Mest, Gtl2, Grb10, Plagl1, and Igf2.

Knockdown of p57, which is the most differentially expressed imprinted gene between normal and mutant BASCs,

  • partially rescues the self-renewal defect of lung stem cells.

Interestingly, insufficient levels of p57 also inhibit self-renewal of lung stem cells. Because p57 expression

  • remains monoallelic in Bmi1 knockdown cells,
  • Bmi1 is thought to maintain an appropriate level of expression from the expressed allele of p57.15

Another IGN member- delta-like homologue 1 (Dlk1) has been shown to be important for postnatal neurogenesis. Interestingly, in this context,

  • Dlk1 loses its imprinting in postnatal neural stem cells and niche astrocytes.16

These studies suggest that modulating IGN may represent another

  • epigenetic mechanism for balancing self-renewal and differentiation in somatic stem cells.

Thus, somatic stem cells either co-opt or remodel these developmental pathways involving the IGN

  • to fulfill the needs of tissue homeostasis during the adult stage.

In summary, several factors participate in regulating the epigenome of somatic stem cells.

Perturbations in the epigenome of somatic stem cells,

  • either during organismal aging or under pathological conditions,

will tip the balance between self-renewal and differentiation of somatic stem cells (Fig. 1). A detailed understanding of the mechanisms underlying these changes will likely result in novel therapeutic approaches targeting somatic stem cells.

Figure 1. The epigenome of somatic stem cells is regulated by diverse factors.

Future perspectives The epigenetic mechanisms governing self-renewal and differentiation of somatic stem cells are likely to be complex because of the diverse needs of different tissues. It would be interesting to determine whether a common mechanism, such as the IGN, exists across different somatic stem cells. Additionally, study- ing epigenetic pathways that are specific to one type of somatic stem cell requires the isolation of these cells and their differentiated progeny, which is more practical in model organisms than in humans. Along these lines, developing robust in vitro culture methods for human somatic stem cells and protocols for differentiating these cells into specific lineages are critical for uncovering epigenetic pathways that are unique to human somatic stem cells. In recent years, the field has seen a great improvement in methods of directed differentiation of human embryonic stem cells and induced pluripotent stem cells (iPSCs). For example, it is relatively straightforward to produce high-purity cell populations that resemble neural stem cells or mesenchymal stem cells from iPSCs.17

These methodologies not only are useful for studying the normal function of somatic stem cells, but also provide an exciting opportunity for understanding the role of somatic stem cells in disease pathology and a platform to screen for drugs. A recent study under- scored the usefulness of this approach. Liu et al. studied neural stem cells derived from Parkinson’s disease human iPSCs and uncovered previously unknown defects in nuclear morphology and epigenetic regulation in these derived NSCs.18 The cellular defects only menifest in “aged” neural stem cells, which is consistent with the fact that Parkinson’s disease pri- marily manifests in old age. More  importantly, this study identified neural stem cell as a potential target of therapeutic intervention for Parkinson’s disease.

Targeted modification of the human genome is  another technological advancement that is on the horizon to greatly facilitate the dissection of epige- netic pathways in somatic stem cells. Although gene targeting in somatic stem cells has been historically challenging, there have been encouraging successful reports following development of new genome-e diting technologies, such as Helper-dependent adenovi- ral vectors, TALENs, and CAS9/CRISPR. With the development of these new technologies, it seems that the stage has been set for a new wave of discoveries in epigenetic mechanisms of somatic stem cells.


1. Li M, Liu GH, Izpisua Belmonte JC. Navigating the epigenetic landscape of pluripotent stem cells. Nat Rev Mol Cell Biol. 2012;13(8):524–535.

2. Hodges E, Molaro A, Dos Santos CO, et al. Directional DNA methylation changes and complex intermediate states accompany lineage specificity in the adult hematopoietic compartment. Mol Cell. 2011;44(1):17–28.

3. Bocker MT, Hellwig I, Breiling A, Eckstein V, Ho AD, Lyko F. Genome- wide promoter DNA methylation dynamics of human hematopoietic progen- itor cells during differentiation and aging. Blood. 2011;117(19):e182–e189.

4. Beerman I, Bock C, Garrison BS, et al. Proliferation-dependent alterations of the DNA methylation landscape underlie hematopoietic stem cell aging. Cell Stem Cell. 2013;12(4):413–425.

5. Wahlestedt M, Norddahl GL, Sten G, et al. An epigenetic component of hematopoietic stem cell aging amenable to reprogramming into a young state. Blood. 2013;121(21):4257–4264.

6. Wu H, Coskun V, Tao J, et al. Dnmt3a-dependent nonpromoter DNA methylation facilitates transcription of neurogenic genes. Science. 2010; 329(5990):444–448.

7. Tadokoro Y, Ema H, Okano M, Li E, Nakauchi H. De novo DNA meth- yltransferase is essential for self-renewal, but not for differentiation, in hematopoietic stem cells. J Exp Med. 2007;204(4):715–722.

8. Challen GA, Sun D, Jeong M, et al. Dnmt3a is essential for hematopoietic stem cell differentiation. Nat Genet. 2011;44(1):23–31.

9. Broske AM, Vockentanz L, Kharazi S, et al. DNA methylation protects hematopoietic stem cell multipotency from myeloerythroid restriction. Nat Genet. 2009;41(11):1207–1215.

10. Trowbridge JJ, Snow JW, Kim J, Orkin SH. DNA methyltransferase 1 is essential for and uniquely regulates hematopoietic stem and progenitor cells. Cell Stem Cell. 2009;5(4):442–449.

11. Wood AJ, Oakey RJ. Genomic imprinting in mammals: emerging themes and established theories. PLoS Genet. 2006;2(11):e147.

12. Lui JC, Finkielstain GP, Barnes KM, Baron J. An imprinted gene network that controls mammalian somatic growth is down-regulated during postna- tal growth deceleration in multiple organs. Am J Physiol Regul Integr Comp Physiol. 2008;295(1):R189–R196.

13. Berg JS, Lin KK, Sonnet C, et al. Imprinted genes that regulate early mam- malian growth are coexpressed in somatic stem cells. PLoS One. 2011; 6(10):e26410.

14. Besson V, Smeriglio P, Wegener A, et al. PW1 gene/paternally expressed gene 3 (PW1/Peg3) identifies multiple adult stem and progenitor cell popu- lations. Proc Natl Acad Sci U S A. 2011;108(28):11470–11475.

15. Zacharek SJ, Fillmore CM, Lau AN, et al. Lung stem cell self-renewal relies on BMI1-dependent control of expression at imprinted loci. Cell Stem Cell. 2011;9(3):272–281.

16. Ferron SR, Charalambous M, Radford E, et al. Postnatal loss of Dlk1 imprinting in stem cells and niche astrocytes regulates neurogenesis. Nature. 2011;475(7356):381–385.

17. Li W, Sun W, Zhang Y, et al. Rapid induction and long-term self-renewal of primitive neural precursors from human embryonic stem cells by small molecule inhibitors. Proc Natl Acad Sci U S A. 2011;108(20):8299–8304.

18. Liu GH, Qu J, Suzuki K, et al. Progressive degeneration of human neural stem cells caused by pathogenic LRRK2. Nature. 2012;491(7425):603–607.


Additional References in Leaders in Pharmaceutical Intelligence

Proteomics and Biomarker Discovery

Developments in the Genomics and Proteomics of Type 2 Diabetes Mellitus and Treatment Targets

Immune activation, immunity, antibacterial activity

Ubiquitin-Proteosome pathway, Autophagy, the Mitochondrion, Proteolysis and Cell Apoptosis: Part III

Ubiquinin-Proteosome pathway, autophagy, the mitochondrion, proteolysis and cell apoptosis

Research on inflammasomes opens therapeutic ways for treatment of rheumatoid arthritis

Update on mitochondrial function, respiration, and associated disorders

MIT Scientists on Proteomics: All the Proteins in the Mitochondrial Matrix identified

Mitochondrial Damage and Repair under Oxidative Stress

Bzzz! Are fruitflies like us?

Discovery of Imigliptin, a Novel Selective DPP-4 Inhibitor for the Treatment of Type 2 Diabetes

Molecular biology mystery unravelled

Gene Switch Takes Blood Cells to Leukemia and Back Again

Wound-healing role for microRNAs in colon offer new insight to inflammatory bowel diseases

Targeting a key driver of cancer

Tang Prize for 2014: Immunity and Cancer

Confined Indolamine 2, 3 dioxygenase (IDO) Controls the Hemeostasis of Immune Responses for Good and Bad                             Demet Sag, PhD

3:45 – 4:15, 2014, Scott Lowe “Tumor suppressor and tumor maintenance genes”

12:00 – 12:30, 6/13/2014, John Maraganore “Progress in advancement of RNAi therapeutics”

9:30 – 10:00, 6/13/2014, David Bartel “MicroRNAs, poly(A) tails and post-transcriptional gene regulation.”

10:00 – 10:30, 6/13/2014, Joshua Mendell “Novel microRNA functions in mammalian physiology and cancer”

Aviva Lev-Ari, PhD, RN

Targeted genome editing by lentiviral protein transduction of zinc-finger and TAL-effector nucleases          Aviva Lev-Ari, PhD, RN

Illana Gozes discovered Novel Protein Fragments that have proven Protective Properties for Cognitive Functioning

Aviva Lev-Ari, PhD, RN






Read Full Post »

Larry, H. Bernstein, MD, FCAP, Author and Curator

Picking the Lock on Pluripotency

Kevin Eggan, Ph.D.
N Engl J Med 28 Nov 2013; 369:2150-2151

Induced pluripotent stem (iPS) cells are obtained by reprogramming somatic cells. This powerful disease-modeling research tool is central to certain experimental approaches to therapy. A recent study showed that iPS cells can be generated with a very high degree of efficiency.


nejmcibr1311880_f1   A Potent Provision of Induced Pluripotent Stem Cells.

A Potent Provision of Induced Pluripotent Stem Cells

Generation of Induced Pluripotent Stem Cells from CD34+ Cells across Blood Drawn from Multiple Donors with Non-Integrating Episomal Vectors

AA Mack, S Kroboth,  D Rajesh,  Wen Bo Wang
Published: November 22, 2011  PLoS ONE 6(11): e27956

The methodology to create induced pluripotent stem cells (iPSCs) affords the opportunity to generate cells specific to the individual providing the host tissue. However, existing methods of reprogramming as well as the types of source tissue have significant limitations that preclude the ability to generate iPSCs in a scalable manner from a readily available tissue source. We present the first study whereby iPSCs are derived in parallel from multiple donors using episomal, non-integrating, oriP/EBNA1-based plasmids from freshly drawn blood. Specifically, successful reprogramming was demonstrated from a single vial of blood or less using cells expressing the early lineage marker CD34 as well as from unpurified peripheral blood mononuclear cells. From these experiments, we also show that proliferation and cell identity play a role in the number of iPSCs per input cell number. Resulting iPSCs were further characterized and deemed free of transfected DNA, integrated transgene DNA, and lack detectable gene rearrangements such as those within the immunoglobulin heavy chain and T cell receptor loci of more differentiated cell types. Furthermore, additional improvements were made to incorporate completely defined media and matrices in an effort to facilitate a scalable transition for the production of clinic-grade iPSCs.

Citation: Mack AA, Kroboth S, Rajesh D, Wang WB (2011) Generation of Induced Pluripotent Stem Cells from CD34+ Cells across Blood Drawn from Multiple Donors with Non-Integrating Episomal Vectors. PLoS ONE 6(11): e27956. doi:10.1371/journal.pone.0027956


Fibroblasts have been a predominate source material for the development of the process to generate induced pluripotent stem cells (iPSCs) given their ability to expand, endure for multiple passages in culture, and receptiveness to efficient infection by viruses expressing a combination of transcription factors for reprogramming [1]–[6]. However, fibroblasts from skin biopsies require invasive surgical procedures, are labor intensive and isolating a sufficient number for reprogramming takes time. In addition, the ability to generate iPSCs from skin appears inversely correlated with the age of the donor likely due to increasing exposure to external mutagens [7]. There is value, therefore, in alternative tissue sources to generate iPSCs that minimize the risk for additional mutation, involve less invasive procedures, and are amenable to industrialization to increase availability across an extensive population range.


A blood draw is an ideal starting point to generate donor-specific iPSCs because it is minimally invasive and established procedures are already in place for acquisition and handling [8]. Lymphocytes comprise a large fraction of the peripheral blood mononuclear cell (PBMC) population but pose at least two potential limitations. First, they are subject to intrinsic DNA rearrangements such as those that occur in B and T cells at the V, D, and J gene segments as well as T cell receptor (TCR) loci to generate a diverse repertoire of antigen-specific surface immunoglobulins. These rearrangements are subsequently perpetuated in iPSCs generated from them and their impact on iPSC function is currently unknown [9], [10]. Second, some research has indicated that host cell types may influence functional properties of iPSCs [11], [12]. For example, while embryonic stem (ES) cells and progenitor cells derived from bone marrow successfully differentiate into B cells, iPSCs derived from B cells have demonstrated resistance to this ability [13], [14], [15]. Therefore, choosing an early lineage cell type that lacks DNA rearrangements alleviates the potential risk of reduced ability to differentiate.

Somatic cells that are characteristically more progenitor-like with respect to the expression of early lineage markers, such as CD34, appear more susceptible to reprogramming, and they too can be isolated from blood [16]. For example, Haase and colleagues successfully isolated and reprogrammed early progenitor cells isolated from cord blood. The ability to reprogram cells from peripheral blood, however, expands the range of host cells available for reprogramming especially when acquisition of cord blood-derived material is not an option. However, the amount of CD34+ cells represents less than 0.1% of the population of PBMCs thus limiting the amount of source material available for reprogramming. To generate enough starting material to perform reprogramming trials, Loh and colleagues relied on patients treated with granulocyte-colony stimulating factor (G-CSF) to expand the number of CD34+ cells in circulating peripheral blood and ultimately generated iPSCs from these cells [17]. The acquisition of blood that does not require donors to receive these agents would be more desirable to avoid the negative side effects associated with them [18]. Studies have also shown that cells from mobilized blood demonstrate functional differences when compared with cells from non-mobilized samples indicating changes to properties intrinsic to the cell [19]. For example, epigenetic and genetic anomalies (i.e. aneuploidy) have been detected in cells derived from patients mobilized with G-CSF [20], [21]. These observations increase the likelihood that similar genetic modifications would carry over into iPSCs generated from mobilized CD34+ cells and potentially impact their function.

The method to generate iPSCs from cord and mobilized peripheral blood has predominately relied on viral-based methods to introduce reprogramming factors [16], [17]. Resulting clones thus have transgenes integrated into their genomes that may alter the function of iPSCs, increase the risk of cancer, and hinder their potential for clinical application. Improvements to reprogramming methods have been made to eliminate integrated transgenes including a recent study examining the reprogramming potential of blood-derived cells using a previously described episomal, oriP/EBNA1-based transfection method [2], [22], [23]. We capitalized on the oriP/EBNA1-method but made modifications to accommodate the reprogramming of CD34+ cells derived from actual vials of blood collected across multiple donors. The oriP/EBNA1-based vectors contribute to the replication and retention of plasmids during each cell division long enough for reprogramming to occur and are lost over time resulting in cells free of transfected DNA and integrated transgenes [24], [25]. Importantly, reprogramming can be achieved through a single transfection. Herein we demonstrate the ability to generate iPSCs from a single vial of blood or less using an improved process of reprogramming that incorporates fully defined conditions to generate iPSCs free of gene rearrangements and transgene elements.



Hematopoietic progenitor cells from non-mobilized, peripheral blood are expandable


Hematopoietic progenitor cells expressing CD34 represent a small fraction of the population and limit the number of cells available for reprogramming; therefore, a modified formulation of a media used to expand cord blood cells was tested on CD34+ cells isolated from peripheral blood [26]. CD34+ cells from two non-mobilized, peripheral (PB.1 and PB.2) blood donors were tested in comparison to CD34+ cells from two cord blood donors (CB.1 and CB.2). Cells were placed into untreated, 24-well culture plates at 1.2×104 per ml of expansion media (StemSpan basal medium; 300 ng/ml each of SCF, Flt3, TPO; 100 ng/ml IL-6; 10 IL-3) and fed 3 to 4 days later. The total number of cells from peripheral blood expanded 170-fold and 680-fold from cord blood after 10 to 14 days in culture (Figure 1A, left hand panel). The percentage of CD34+ cells in the peripheral and cord blood samples peaked in less than 1 week (CB data not shown; Figure 1A, right hand panel). The expression profile of expanding populations was determined by flow cytometry for all donors examined herein (Figure 1B). Phenotypic analysis of the resulting peripheral blood culture PB.2 after 10 days of expansion revealed predominately early progenitor cells expressing CD43, CD45, CD33, CD44, CD15, and CD117 and marginal levels of T (CD3, CD4, CD8), NK (CD56, CD94), B (CD19), macrophage (CD163), megakaryocyte (CD41), and monocyte (CD14) cells (Figure 1C).

Figure 1. Hematopoietic cells enriched for CD34 expression are expandable.

journal.pone.0027956.g001  Figure 1. Hematopoietic cells enriched for CD34 expression are expandable


A. Graphs depict the expansion of purified cells from either two peripheral (PB.1 and PB.2) or cord (CB.1 or CB.2) blood donors over time (lefthand panel) along with the percentages of the total population that are CD34+ (righthand panel). B. Representative profile of a purified population of cells after 6 days of expansion by flow cytometry on cells isolated from Donor 3002. Flow cytometry plots from control staining using IgG antibodies (upper plots) are compared to plots with antibodies specific to lineage markers (lower plots). C. The graph represents an extended analysis by flow cytometry of the characteristic profile of PB.2 cells after 10 days of expansion. The % positive indicates the fraction of the population expressing the cell surface markers on the x-axis. D. The total number of CD34+ cells across 16 different donors was assessed beginning at 0 and 6 days of expansion (left side y-axis). The fold expansion (right side y-axis, orange squares) was determined by dividing the total number of cells at day 6 divided by the number of cells at day 0 after purification. The average percent of CD34 expression across all 16 donors was 48+/−19%.

These expansion conditions were then applied to cells acquired from a single vial of blood collected from multiple donors. PBMCs were isolated, frozen down immediately, or directly purified for CD34+ cells and seeded for expansion. On average, 1×107 PBMCs were recovered per 8 ml vial of blood and yielded approximately 2×104 cells after purification for CD34-expressing cells (data not shown). Although the magnitude of expansion was variable, cells from all of the donors demonstrated expansion ranging from 3 to 83-fold after 6 days in culture and approximately 48+/−19% of that population expressed CD34 (Figure 1D).

Optimizing the generation of iPSCs with small molecules and a defined matrix

The total number of purified cells isolated from a single, 8 ml vial of blood can be as little as 2×104 CD34+ cells; therefore, a range of CD34+ cell numbers was tested to determine transfection efficiency from low cell numbers. The efficiency of transfection was determined by transfecting an oriP/EBNA1-containing plasmid encoding GFP into expanded CD34+ cells and assessing them by flow cytometry. Viability was determined by identifying the fraction of viable cells that did not stain positively for trypan blue the day after transfection divided by the total number of input cells. Viability was approximately 30% when 1×104 to 1×105 input cells were used for transfection (data not shown). Cell numbers at 1×104 and 3×104 resulted in an efficiency of 30% and was 40% when using 6×104 and 1×105 cells (Figure 2A). Cells expanded for only 3 days demonstrated a two-fold increase in transfection efficiency. Over 90% of those cells also co-expressed GFP and CD34 while only 18% of the cells transfected after 6 days of expansion co-expressed both markers (Figure 2B, C). These results support the notion that the conditions selected for this protocol favor the transfection of CD34+ cells present in the population.

Figure 2. Identifying optimal transfection conditions for CD34+ cells.

journal.pone.0027956.g002   Figure 2. Identifying optimal transfection conditions for CD34+ cells.

A. PBMCs (donor GG) were isolated and purified for CD34-expression and expanded for 6 days. A range of cell numbers were transfected with a control, oriP/EBNA1-based plasmid expressing GFP. Transfection efficiency was determined by calculating the percentage of viable cells expressing GFP detectable by flow cytometry (n = 6). B. PBMCs (donor A2389) were isolated, purified for CD34-expression and expanded for 3 or 6 days. 6×104 to 1×105 cells were transfected with the control, GFP-expressing plasmid. The graph depicts the percent of the total population that is GFP-positive along with the absolute number of total cells (n = 3). C. The graph represents the fraction of cells in B that co-express GFP and CD34 when transfected at 3 or 6 days of expansion (n = 3).    http//

We anticipated variability in reprogramming efficiency given the differences already observed across donors for other cell types tested and with other methods of reprogramming. Therefore, we optimized the matrix, media, and plasmid combinations used for reprogramming. Firstly, a common source of variation occurs when MEFs or matrigel are used because both are undefined, cumbersome to prepare, and vary from lot-to-lot. Therefore, we established a defined matrix by testing a variety of commercially available possibilities. Recombinant protein fragments containing the active domains of human fibronectin (RetroNectin) or vitronectin consistently supported iPSC formation the best among those tested. Second, the efficiency of colony formation on RetroNectin-coated plates improved significantly when used in combination with StemSpan SFEM media, N2, B27, and a cocktail of small molecules that included PD0325901, CHIR99021, A-83-01, and HA-100 (Figure 3A). These molecules have been described previously as inhibitors of MEK, GSK3β, TGFβ, and ROCK pathways, respectively [27], [28]. Patches of adherent cells appeared within one week and became a positive indicator for progression into iPSCs since hematopoietic cells are typically cultured in suspension. The following week many of the colonies exhibited overt characteristics typical of an iPSC and stained positively for the common pluripotency markers Tra-1-81 and alkaline phosphatase (AP) (Figure 3A). The borders of the colonies were compact and the nucleoli more visible when cultures were transitioned to defined, TeSR2 media without small molecules 1.5 to 2 weeks following transfection. Thirdly, episomal oriP/EBNA1-based plasmids were used to deliver Oct4, Sox2, Klf4, C-myc, Nanog, Lin28, and SV40 Large T-antigen as previously described (Set 1; Figure 3B) [2]. Different combinations of reprogramming plasmids were also tested to determine whether a boost in reprogramming efficiency was possible. Based on previous reports indicating the benefit of L-myc in reprogramming trials, we modified plasmid combination Set 1 and substituted L-myc in place of C-myc (Set 2, Figure 3B) [29], [30]. While an improvement was not observed when C-myc was substituted for L-myc in the combination of plasmids represented in Set 1 (data not shown), an equal to or two-fold improvement was observed with plasmid Set 2 expressing L-myc (Figure 3C). Optimizing a range of input cell numbers for transfection also revealed more consistent generation of iPSCs when transfecting greater than 5×104 cells (Figure 3D).

Figure 3. Plasmid transfections to optimize reprogramming efficiency.

journal.pone.0027956.g003  Figure 3. Plasmid transfections to optimize reprogramming efficiency.

A. Representative reprogramming trial from freshly drawn blood (donor 3002) using combination plasmid Set 2 for transfection. A single well is shown from a 6-well plate that contains colonies staining positively for AP activity (i). The white arrowhead highlights the colony magnified in panel ii that also stained positively for Tra-1-81 expression (green), panel iii. B. Schematic of the plasmid sets successfully used for reprogramming trials. Set 1 contains a combination of two plasmids for transfection whereby a 20 kb plasmid that either contains C- or L-myc is depicted. Set 2 includes a three plasmid combination for transfection. C. CD34+ cells purified from four different donors were expanded for 6 days and transfected using the plasmid combination that expresses either Set 1 or Set 2 plasmid sets to compare the total number of resulting iPSCs. D. Reprogramming trials were performed using plasmid Set 2 to transfect a range of cell numbers expanded for 6 days (donor GG, n = 6).


Optimization of iPSCs generated from CD34+ cells isolated from fresh, whole blood across multiple donors

The next step was to confirm iPSCs could be generated from actual vials of human blood, ensure cell numbers optimized for expansion and transfection are applicable across multiple donors, and determine whether the starting volume of blood can be minimized. Colonies emerging during reprogramming were scored positive by their ability to express Tra1-81 and exhibit a classic embryonic stem (ES) cell-like morphology. After colonies were picked from the reprogramming cultures, a subset of them were further characterized to confirm their pluripotency. Reprogramming efficiency was calculated in two ways 1) the number of iPSCs divided by the total volume of blood collected from each donor and 2) the total number of iPSCs divided by the number of cells for transfection multiplied by 1×105 cells. The first calculation incorporates the whole process beginning from the blood collection to the generation of an iPSC. The second calculation removes the variability incurred during the isolation of PBMCs, purification, and expansion and focuses on the number of iPSCs generated per number of cells placed into transfection.

We tested blood collected across donors spanning a range of ethnicities, ages, and genders to confirm iPSCs could be generated from CD34+ cells purified from fresh blood draws (Table 1). Six of these donors provided up to 55 ml of blood, and PBMCs from them were either isolated and used directly for purification, expansion, and reprogramming or frozen down after isolation. iPSCs were successfully generated from all six donors regardless of whether they were from fresh or frozen cells despite the lower efficiency of reprogramming, less than 1 iPSC per ml of blood (Table 2). Next, smaller volumes of blood representative of a single vial were obtained from six different donors to test parameters established in earlier experiments such as the number of cells for transfection and plasmid combinations. The cell numbers used for transfection from donors 3052, 3233, and 3373 ranged from 2×104 to 4×104 which fall below the minimum, 5×104 cells, established with our optimization studies. These experiments resulted in less than one iPSC per ml of blood (Table 2). Also, transfections with cells from donors 2583, 2970, and 3185 represent early trials performed with plasmid Set 1 expressing C-myc before Set 2 plasmids expressing L-myc were fully optimized which may have resulted in more iPSCs.

Table 1. Diversity across the set of donors used for reprogramming trials to generate iPSCs.

Table 2. Optimizing reprogramming from a range of blood volumes across multiple donors.

The next step was to then extend the insights acquired from these donors and verify the robustness of our protocol against ten new donors. The average number of iPSCs per ml of blood and per 1×105 cells across these donors indeed improved after incorporating experience with handling and the testing performed on the earlier donor samples (Table 3). Furthermore, these experiments were extended to test even smaller volumes of blood from a subset of the same donors in Table 3. CD34+ cells purified from approximately 4 ml of blood were sufficient to generate iPSCs from all six donors tested, and CD34+ cells from approximately 2 ml of blood from four out of six of these donors generated iPSCs (Table 4). These results demonstrate that iPSCs can be generated from CD34+ isolated from tractable volumes of blood using this non-intergrating and feeder-free method of reprogramming.

Table 3. Improved reprogramming across multiple donors from a single vial of blood.


Table 4. The efficiency of reprogramming CD34+ cells beginning from 4 ml of blood or less.


iPSCs derived from peripheral blood are pluripotent and free of transgene elements

Multiple iPSCs from each of the donors that were reprogrammed from Tables 2, 3, and 4 were selected for further characterization to confirm their pluripotency. The clones exhibited a normal karyotype, were positive for Tra-1-81 and SSEA-4 expression by flow cytometry as well as endogenous genes DNMT3B, REX1, TERT, UTF1, Oct4, Sox2, Nanog, Lin28, Klf4, and C-myc (Table 5, Figure 4A–C). Clones did not exhibit integrated transgene or episomal elements and loss of episomal DNA occurred, on average, within 7–10 passages (Table 5, Figure 4D,E). A PCR screen did not reveal rearrangements pertaining to immunoglobulin heavy chain (IgH) or a subset of T cell receptor (TCR) gene segments (Table 5, Figure 4F). The lack of rearrangements supports the notion that the protocol selectively favors the production of iPSCs from hematopoietic progenitors rather than more differentiated cell types. When used for in vitro directed differentiation at passage 15, donor 2939 iPSC clones 4 and 5, which have lost episomal plasmids, were competent to form neurons (Figure 5G). Furthermore, five iPS clones from three different donors also formed teratomas after injection into immunodeficient (SCID) mice (Figure 4G). Interestingly, the presence of residual episomal plasmids did not appear to hinder the ability to form teratomas since clone 6 from donor 2970 did not lose transfected plasmids until passage 18, well after injection into mice for teratoma studies.

Figure 4. Characterization of iPSCs derived from CD34+ blood cells.

journal.pone.0027956.g004  Figure 4. Characterization of iPSCs derived from CD34+ blood cells.

A subset of iPSC clones were characterized for pluripotency. The experiments demonstrated in this figure provide representative examples of the types of results observed for characterization studies using iPS clones 4 and/or 5 derived from donors 2939 and 3389. A. Cytogenetic analysis on G-banded metaphase cells from iPS clone 4 exhibiting a normal karyotype. B and C. RT-PCR confirms the endogenous expression of classic pluripotency genes and the absence of expression from transgenes. A standard in-house iPS line served as the positive control k. D. Clones were deemed free of episomal (E) DNA and genomic integration (G) by PCR. E. PCR was used to track the loss of oriP/EBNA1-based plasmids at multiple passages using primers that amplify EBNA1. A control plasmid at 1 and 20 copies per genome was used to establish the sensitivity of the PCR at 1 copy per 3,000 cells. F. PCR screen using primers specific for the joining region and all three of the conserved framework regions (FR1, FR2 and FR3) to amplify immunoglobulin heavy chain (IgH) gene rearrangements and two assays with primers specific to the T cell receptor (TCR) gamma gene rearrangement. G. Representative image of donor 2939 clone 5 differentiated in vitro into neurons (i). Clone 5 also demonstrated differentiation into all three germ layers: ii) epithelium iii) endoderm iv) mesoderm v) ectoderm vi) endoderm from teratomas formed when iPSCs were injected into immunodeficient, SCID mice.

Figure 5. The presence of CD34+ cells correlates with reprogramming efficiency.

journal.pone.0027956.g005  Figure 5. The presence of CD34+ cells correlates with reprogramming efficie

A. CD34+ cells from four different blood donors were expanded for 3, 6, 9, or 13 days. A large volume of blood was collected from donors 3096, 2849, and 3389 to ensure sufficient cell numbers to perform these studies. Expanding CD34+ cells using plasmid DNA combination Set 2. The efficiency of reprogramming was calculated as the total number of iPSCs exhibiting morphological features characteristic of an ES cell and an ability to stain positively for Tra-1-81 divided by the total number of cells used for transfection. Black Squares depict the percentage of the population expressing CD34 at the indicated days of expansion. B. Representative reprogramming trial whereby both the positive (i) and negative (ii) fraction following purification were used for reprogramming. Panel (i) shows one well of a 6-well plate that contains successfully reprogrammed colonies from donor 2939 based on their ability to demonstrate AP activity. The CD34-depleted fraction from donor 2939 was unable to form colonies as indicated by the lack of AP staining when performed in parallel with the purified population panel, ii. Panels iii and iv magnify the colony in panel (i) marked by a white arrowhead and demonstrates expression of Tra-1-81 (green), panel iv.

Table 5. Characterization of insert-free iPS clones derived from fresh blood.

Reprogramming efficiency correlates with the amount of CD34-expression

The isolation of CD34+ cells from PBMCs creates an additional step in our process and others have demonstrated successful reprogramming directly from PBMCs without the need for purification [22]. Therefore, several experiments to determine whether a correlation exists between CD34+ cells and reprogramming efficiency were performed. First, the expanding CD34+ populations were screened by flow cytometry for characterization prior to transfection. T, B, and NK cells were undetectable after 3 and 6 days of expansion demonstrated by their lack of CD3, 19, and 56 expression, respectively. The percentage of CD34 expression during expansion ranged from 30 to 100%, thus increasing the likelihood of reprogramming more of an early lineage cell type (n = 9, data not shown). Second, samples were taken from the purified populations during expansion at different timepoints as they lost CD34 expression to determine their receptiveness to reprogramming. A decrease in reprogramming efficiency was observed in correlation with decreasing percentages of CD34 expression across populations of cells from four independent donors (Figure 5A). For example, donor 3096 exhibited only 1 iPSC per 1×105 input cells when beginning from cells expanded for 13 days (31% CD34+) compared to 91.5 iPSCs per 1×105 cells following 3 days when levels of CD34 expression were much higher, 98% (Figure 5B). Third, populations depleted of CD34+ cells were tested for their ability to reprogram in parallel with their CD34+ cell counterparts. These CD34-depleted populations were not receptive to reprogramming as the CD34+ cells even when the same media and transfection conditions were used (n = 3, Figure 5B). Finally, a side-by-side comparison of reprogramming efficiency was performed between unpurified PBMCs and CD34+ cells isolated from 10 different donors. A medium described previously for the expansion of erythroblasts and for successful reprogramming studies was used to ensure media would not be a limiting factor for reprogramming the PBMCs in our protocol [22], [31]. Reprogramming trials beginning from either PBMCs or CD34+ cells were launched in parallel using their respective media for expansion. The efficiency of reprogramming was approximately 2 to 8 fold higher when beginning with cells purified for CD34 expression in 9 out of the 10 donors compared to those from PBMCs (p = 0.007; Figure 6). A significant fraction of the PBMC population was comprised of lymphocytes (~79+/−14% CD3/CD19) at the time of transfection (data not shown); therefore, PCR was performed to screen for potential IgH and TCR gene rearrangements to determine whether both protocols promoted the generation of iPSCs free of gene rearrangements. Interestingly, screened clones from either cell type were free of IgH and TCR gene rearrangements indicating that both protocols favor the reprogramming of early progenitor cells. The lower efficiency of reprogramming from the PBMC population may reflect the dilution of early progenitor blood cells by a predominately lymphocytic population.

Figure 6. Comparing the efficiency of reprogramming between PBMCs and CD34+ cells.

PBMCs and CD34+ cells were isolated from single tubes of blood provided from 10 different donors. The efficiency of reprogramming following transfection with DNA Combination Set 2 was determined for each donor and each method. “Total colonies” refers to all iPS colonies derived from either CD34+ or PBMC populations that stain positively for Tra1-60 and “iPS-like” colonies are those that stain positively for Tra1-60, exhibit clear iPS morphology, and are large enough to pick for expansion. Input cells refer to the number of CD34+ cells or PBMCs were used for transfection. The efficiencies across all donors from both methods were compared using the Wilcoxon signed rank test (two-sided), p = 0.007.

Generation of iPSCs from CD34+ cells using completely defined reagents

Additional reprogramming trials were performed using completely defined conditions to enable the production of clinic-grade iPSCs. A large pool of CD34+ cells mixed from multiple donors was used for multiple tests and resulted in a successful expansion of 113+/−11 fold in defined media compared to 83+/−32 fold for cells in standard conditions after 6 days of expansion (Figure 7A). Despite the 30-fold difference between the two conditions, the absolute number of CD34+ cells is similar between the two populations when multiplied by the percentage of the population expressing CD34 by flow cytometry. For example, 42+/−13% of the population expanded in standard conditions expressed CD34 and 26+/−16% expressed CD34 using completely defined conditions (Figure 7A). There were no detectable CD3+, CD19+, or CD56+ cells after 6 days in culture consistent with our earlier expansion trials (data not shown). The media used for reprogramming is completely defined with the exception of the supplement B27 which contains bovine serum albumin (BSA). However reprogramming was still achieved in the presence or absence of B27 (Figure 7B). These improvements coupled with a defined matrix enables the production of iPSCs in a completely defined process.

Figure 7. The generation of iPSCs from blood using completely defined conditions.


A. Fold expansion of CD34+ cells pooled from multiple blood donors in standard (n = 13) and completely defined conditions (n = 2) after 6 days of expansion. Fold expansion was calculated from the total number of cells at day 6 divided by the number of cells the day after purification. Percentages indicate the fraction of cells expressing CD34 in the total population as assessed by flow cytometry. B. Reprogramming trials were performed on CD34+ cells obtained by leukapheresis from donors GG and A2389 with and without the B27 supplement.


CD34+ cells possess characteristics that make them an ideal blood cell to reprogram: they are readily identified, highly receptive to reprogramming, and free of gene rearrangements characteristic of more differentiated cell types. However, their low numbers in circulating blood have made them a less desirable cell type for reprogramming because large volumes of blood were predicted to be required for the generation of iPSCs. We describe a method to generate insert-free iPSCs from CD34+ cells beginning from a single vial of blood or less. In some cases, the number of CD34+ cells that expanded exceeded that required for a single transfection after 6 days in culture making it possible to transfect after only 3 days of expansion, shorter than the amount of time Chou and colleagues used to reprogram unpurified PBMCs [22].

Proliferation contributes to reprogramming efficiency; therefore, choosing a culture medium that promotes proliferation will effectively promote reprogramming. Data presented in our manuscript supports this assertion because all four donor populations examined were almost 100% positive for CD34 expression after 3 days in culture, but they were not equally receptive to reprogramming (Figure 5A). Cells from donor 3096 demonstrated the highest fold expansion following purification as well as the highest efficiency of reprogramming. However, we go on to demonstrate that proliferation is not the only contributor to efficient reprogramming. In our experiments, the purity of the cell population diminishes over time following purification, but the cells within the culture continue to expand despite low CD34 expression. Figure 1C shows that this expanding population 10 days following purification consists primarily of early lineage progenitor cells. This result indicates that our medium has the capacity to stimulate the proliferation of non-T and non-B cells that have never been or are no longer CD34+. If proliferation were the primary force driving the efficiency of reprogramming, then it would be expected that proliferating non-T/non-B cells within our population would be equally receptive to reprogramming regardless of their time in culture. Our results are contrary to this hypothesis, however, because the efficiency of reprogramming decreases as the magnitude of CD34 expression decreases. This is observed across four independent donor cell populations and is consistent with dependence of reprogramming on CD34 expression (see Figure 5A). These results, taken together, support our hypothesis that both proliferation and cell identity contribute to the efficiency of reprogramming.

We have also outlined a protocol that begins to systematically address some of the challenges in the generation of clinical-grade iPSCs in an effort to advance their use from research lab to clinic. iPSCs must not only be manufactured consistently from a tractable tissue source but also satisfy safety requirements. The core of these requirements includes the use of completely defined culture conditions and a standardized reprogramming method that results in the removal of the potentially oncogenic transgenes employed to reprogram the cells. The starting point of the protocol is the actual patient sample, a single vial of blood. The ability to begin from frozen rather than fresh starting material allows flexibility to launch multiple reprogramming trials in parallel. We demonstrate that either CD34+ cells or PBMCs may be used as the source population for reprogramming. The iPSCs generated by this method are free of transfected DNA as well as B and T cell gene rearrangements. Several challenges remain, however, before routine production of clinical-grade iPSCs can be completely performed. First, there is considerable variation in the efficiency of reprogramming from donor to donor. Some of this variation is likely due to the inherent differences among the donors, but a careful examination of external sources of variation at each step from blood to iPSCs may well reveal areas in addition to those we have uncovered that can be better controlled. For example, we demonstrate potential in the ability to produce iPSCs using completely defined reagents to minimize variation. Second, an automated method for screening and selecting iPSCs during reprogramming would facilitate high throughput production of iPSCs. Third, a robust production protocol must also include a method for the rapid screening of iPSCs to identify those that both lack potentially harmful mutations and are readily differentiated into various cell types. In sum, the generation of iPSCs using a standardized process beginning from early progenitor cells isolated from routine blood draws minimizes this variation and is a good starting point to provide a more comparable baseline for analysis. We present the first steps towards a standardized process to make the generation of clinical-grade iPSCs a reality.

Materials and Methods

Ethics Statement

All human primary cells were generated in vitro from tissue samples from human donors with appropriate written informed consent given to the commercial providers.

All animal work was conducted according to relevant national and international guidelines under the approval of the Cellular Dynamics International Animal Care and Use Committee. As a private company, our animal facility does not provide a permit number or approval ID since mouse is not a protected species.

Processing whole blood samples

Peripheral (PB.1 and PB.2) and cord (CB.1 and CB.2) blood-derived CD34+ cells were obtained from AllCells (Emeryville, CA USA). Blood collections were performed at AllCells and Meriter Laboratories (Madison, WI USA) using standard, 8 ml Vacutainer Cell Processing Tubes (both sodium citrate and sodium heparin-based tubes are acceptable; BD Biosciences; Franklin Lakes, NJ USA). Appropriate documentation for informed consent was completed prior to blood collection (Meriter Laboratories). Vacutainers were processed within 24 hours of collection. Briefly, the PBMC-containing upper phase was collected and washed with ice-cold PBS (Invitrogen; Carlsbad, CA USA). Cells were either frozen down or used directly for purification with the CD34 MicroBead Kit (Miltenyi; Auburn, CA USA) and used according to the manufacturer’s protocol. Some samples were treated with Histopaque (Sigma Aldrich; St. Louis, MO USA) to minimize the number of red blood cells (RBCs) and centrifuged at 2000 rpm for 20 minutes without braking. The interface containing the PBMCs was removed if samples were treated with histopaque, cells washed again with chilled PBS, centrifuged at 600× g for 15 minutes and either frozen down with CryoStor10 (StemCell Technologies; Vancouver, BC Canada) or used directly for purification. CD34+ cell expansion media: StemSpan SFEM (StemCell Technologies), Flt3, SCF, TPO each at a final concentration of 300 ng/ml, IL-6 (100 ng/ml) and IL-3 (10 ng/ml) (Peprotech; Rocky Hill, NJ USA), supplemented with DNaseI (final concentration at 20 U/ml), and 1× Antibiotic-antimycotic (Invitrogen) for overnight recovery. Defined expansion media: serum-free StemSpan H3000 (StemCell Technologies), animal-free IL-6 (R&D Systems Minneapolis, MN USA), and recombinant human IL-3, TPO, Flt3, and SCF (Peprotech) at the same concentrations listed above. PBMC expansion media: StemSpan SFEM, ExCyte Medium Supplement (Millipore; Billerica, MA), Glutamax (Invitrogen), SCF (250 ng/ml), IL-3 (20 ng/ml), Erythropoietin (2 U/ml; Prospec; Rehovot, Israel), IGF-1 (40 ng/ml; Prospec), and Dexamethasone (1 µM; Fisher; Waltham, MA). PBMCs were resuspended at 1×106 cells/ml for expansion.

Flow cytometry

Cell surface staining of hematopoietic cells was performed with CD45-PE, CD34-APC, CD19-APC and CD56-PE (BD Biosciences) and CD3-PE (eBioscience; San Diego, CA USA) antibodies. iPSCs were processed directly for antibody staining for the presence of Tra-1-81 (Stemgent; Cambridge, MA USA) and SSEA-4 (BD Pharmingen; San Diego, CA USA). Propidium Iodide (Sigma Aldrich) was added for dead cell exclusion, and all stained cells were analyzed in combination with their respective isotype controls using a flow cytometer (Accuri; Ann Arbor, MI USA).

Reprogramming cells enriched for CD34-expression

The CD34 nucleofection kit and device (Lonza; Allendale, NJ USA) were used for transfections. For CD34+ cells, 3.5 µg of each plasmid in Combination Set 1 and 3 ug of each plasmid for Combination Set 2 except for the L-myc containing plasmid where 2 µg was transfected using program U-08. Cells were seeded onto RetroNectin-coated 6-well plates (Takara Bio, Inc; Otsu, Shiga Japan). Seeding density ranged from 5×104 to 1×105 cells/ml. Reprogramming media: StemSpan SFEM (StemCell Technologies) supplemented with non-essential amino acids (NEAA; Invitrogen), 0.5× Glutamax, N2B27 (Invitrogen), 0.1 mM β-mercaptoethanol (Sigma-Aldrich), 100 ng/mL zebrafish basic fibroblast growth factor (zbFGF), 0.5 µM PD0325901, 3 µM CHIR99021, 0.5 µM A-83-01 (all molecules from Stemgent), and 10 µM HA-100 (Santa Cruz; Santa Cruz CA USA). Conditions for PBMC reprogramming relied on 1×106 cells per transfection, program T-16, and DNA from Combination Set 2 at the concentrations described for CD34+ cells. Reprogramming media for PBMCs was the same with the exception of the small molecule cocktail which contained recombinant human LiF (Millipore), 3 uM CHIR99021, and 0.5 uM A-83-01. In general, cultures were fed with fresh medium every other day for 9 to 14 days then transitioned to TeSR2 (Stem Cell Technologies) without the addition of small molecules. iPSC colonies were scored with Tra-1-81 antibody (StainAlive™ DyLight™ 488 Mouse anti-Human Tra-1-81 antibody; Stemgent) or mouse-anti-Tra-1-60 IgM antibody (R&D) in combination with goat anti-mouse IgM Alexa 488 (Invitrogen), and alkaline phosphatase expression (Vector Blue Alkaline Phosphatase Substrate Kit III, Vector Laboratories; Burlingame, CA USA).

Detecting endogenous expression of pluripotency markers

Total RNA was isolated using the RNeasy Mini Plus kit (Qiagen; Valencia, CA USA) per the manufacturer’s protocol. Approximately 1 µg of total RNA was used for cDNA synthesis using the SuperScript III First-Strand Synthesis system for RT-PCR (Invitrogen). RT-PCR was performed using previously described primers and those listed in Table 6 [2]. cDNA was diluted 1:2 and 1 µl was used in reactions with GoTaq Green Master Mix (Promega; Madison, WI USA).

Table 6. Primer sequences for the detection of endogenous gene expression.

journal.pone.0027956.t006  Table 6. Primer sequences for the detection of endogenous gene expression.

Episomal and Genomic DNA isolation

Immunoglobulin heavy chain and T cell gene rearrangements


In Vitro Differentiation and Teratoma Studies


Author Contributions


1. Woltjen K, Michael IP, Mohseni P, Desai R, Mileikovsky M, et al. (2009) piggyBac transposition reprograms fibroblasts to induced pluripotent stem cells. Nature 458: 766–770. doi: 10.1038/nature07863.

2. Yu J, Hu K, Smuga-Otto K, Tian S, Stewart R, et al. (2009) Human Induced Pluripotent Stem Cells Free of Vector and Transgene Sequences. Science.

3. Yu J, Vodyanik MA, Smuga-Otto K, Antosiewicz-Bourget J, Frane JL, et al. (2007) Induced pluripotent stem cell lines derived from human somatic cells. Science 318: 1917–1920. doi: 10.1126/science.1151526.

4. Takahashi K, Tanabe K, Ohnuki M, Narita M, Ichisaka T, et al. (2007) Induction of pluripotent stem cells from adult human fibroblasts by defined factors. Cell 131: 861–872. doi: 10.1016/j.cell.2007.11.019.

5. Okita K, Nakagawa M, Hyenjong H, Ichisaka T, Yamanaka S (2008) Generation of mouse induced pluripotent stem cells without viral vectors. Science 322: 949–953. doi: 10.1126/science.1164270.

6. Saha K, Jaenisch R (2009) Technical challenges in using human induced pluripotent stem cells to model disease. Cell Stem Cell 5: 584–595. doi: 10.1016/j.stem.2009.11.009.

7. Park IH, Zhao R, West JA, Yabuuchi A, Huo H, et al. (2008) Reprogramming of human somatic cells to pluripotency with defined factors. Nature 451: 141–146. doi: 10.1038/nature06534.

8. Yamanaka S (2010) Patient-specific pluripotent stem cells become even more accessible. Cell Stem Cell 7: 1–2. doi: 10.1016/j.stem.2010.06.009.

9. Brown ME, Rondon E, Rajesh D, Mack A, Lewis R, et al. (2010) Derivation of induced pluripotent stem cells from human peripheral blood T lymphocytes. PLoS One 5: e11373. doi: 10.1371/journal.pone.0011373.

10. Hanna J, Markoulaki S, Schorderet P, Carey BW, Beard C, et al. (2008) Direct reprogramming of terminally differentiated mature B lymphocytes to pluripotency. Cell 133: 250–264. doi: 10.1016/j.cell.2008.03.028.

11. Polo JM, Liu S, Figueroa ME, Kulalert W, Eminli S, et al. (2010) Cell type of origin influences the molecular and functional properties of mouse induced pluripotent stem cells. Nat Biotechnol 28: 848–855. doi: 10.1038/nbt.1667.

12. Miura K, Okada Y, Aoi T, Okada A, Takahashi K, et al. (2009) Variation in the safety of induced pluripotent stem cell lines. Nat Biotechnol 27: 743–745. doi: 10.1038/nbt.1554.

13. Nakano T (1995) Lymphohematopoietic development from embryonic stem cells in vitro. Semin Immunol 7: 197–203. doi: 10.1016/1044-5323(95)90047-0.

14. Cho SK, Webber TD, Carlyle JR, Nakano T, Lewis SM, et al. (1999) Functional characterization of B lymphocytes generated in vitro from embryonic stem cells. Proc Natl Acad Sci U S A 96: 9797–9802. doi: 10.1073/pnas.96.17.9797.

15. Wada H, Kojo S, Kusama C, Okamoto N, Sato Y, et al. (2011) Successful differentiation to T cells, but unsuccessful B-cell generation, from B-cell-derived induced pluripotent stem cells. Int Immunol 23: 65–74. doi: 10.1093/intimm/dxq458.

+ more in document

Analysis of Human and Mouse Reprogramming of Somatic Cells to Induced Pluripotent Stem Cells. What Is in the Plate?

Stéphanie Boué, Ida Paramonov,  María José Barrero,  Juan Carlos Izpisúa Belmonte
Published: September 17, 2010

After the hope and controversy brought by embryonic stem cells two decades ago for regenerative medicine, a new turn has been taken in pluripotent cells research when, in 2006, Yamanaka’s group reported the reprogramming of fibroblasts to pluripotent cells with the transfection of only four transcription factors. Since then many researchers have managed to reprogram somatic cells from diverse origins into pluripotent cells, though the cellular and genetic consequences of reprogramming remain largely unknown. Furthermore, it is still unclear whether induced pluripotent stem cells (iPSCs) are truly functionally equivalent to embryonic stem cells (ESCs) and if they demonstrate the same differentiation potential as ESCs. There are a large number of reprogramming experiments published so far encompassing genome-wide transcriptional profiling of the cells of origin, the iPSCs and ESCs, which are used as standards of pluripotent cells and allow us to provide here an in-depth analysis of transcriptional profiles of human and mouse cells before and after reprogramming. When compared to ESCs, iPSCs, as expected, share a common pluripotency/self-renewal network. Perhaps more importantly, they also show differences in the expression of some genes. We concentrated our efforts on the study of bivalent domain-containing genes (in ESCs) which are not expressed in ESCs, as they are supposedly important for differentiation and should possess a poised status in pluripotent cells, i.e. be ready to but not yet be expressed. We studied each iPSC line separately to estimate the quality of the reprogramming and saw a correlation of the lowest number of such genes expressed in each respective iPSC line with the stringency of the pluripotency test achieved by the line. We propose that the study of expression of bivalent domain-containing genes, which are normally silenced in ESCs, gives a valuable indication of the quality of the iPSC line, and could be used to select the best iPSC lines out of a large number of lines generated in each reprogramming experiment.

Citation: Boué S, Paramonov I, Barrero MJ, Izpisúa Belmonte JC (2010) Analysis of Human and Mouse Reprogramming of Somatic Cells to Induced Pluripotent Stem Cells. What Is in the Plate? PLoS ONE 5(9): e12664.

Figure 2. Human protein-protein interaction networks of genes with higher expression levels in ESCs and iPSCs compared to somatic cells.

journal.pone.0012664.g002  Figure 2. Human protein-protein interaction networks of genes with higher expression levels in ESCs and iPSCs compared to somatic cells.


The human protein-protein interaction networks of genes most consistently highly expressed in ESCs and iPSCs, compared to the starting cell populations, have been created from the lists of the biggest changes in expression, using String[71] with high confidence interactions (min score 0.7) and have been edited in Medusa[72]. They show a central, highly interconnected network of genes in which the most famous pluripotency transcription factors are to be found and which is likely to represent the core pluripotency network. They also highlight a number of genes whose functions relate to cell-cell communication, cell cycle, DNA repair and other metabolisms.

Figure 3. Mouse protein-protein interaction networks of genes with higher expression levels in ESCs and iPSCs compared to somatic cells.

journal.pone.0012664.g003  Figure 3. Mouse protein-protein interaction networks of genes with higher expression levels in ESCs and iPSCs compared to somatic cells.

The mouse protein-protein interaction networks of genes most consistently highly expressed in ES and iPSCs, compared to the starting cell populations, have been created from the lists of biggest changes in expression, using String[71] with high confidence interactions (min score 0.7) and have been edited in Medusa[72]. They show a central, highly interconnected network of genes in which the most famous pluripotency transcription factors are to be found and which is likely to represent the core pluripotency network. They also highlight a number of genes those functions relate to cell-cell communication, cell cycle, DNA repair and other metabolisms.



Read Full Post »