Cell Tracking with Lipophilic Membrane Dyes | Biowire Spring 2011

By: Katharine A. Muirhead§, Joseph D. Tario, Jr.†, Paul K. Wallace† , Biowire Spring 2011 — Cell Lines — Models of Disease
†SciGro, Inc., Midwest Office, Madison, Wisconsin, USA †Department of Flow and Image Cytometry, Roswell Park Cancer Institute, Buffalo, New York, USA

Optimal staining is a key component for studying tumorigenesis and progression. Learn useful tips and techniques for dye applications, including examples from recent studies.

Cell Tracking - Lipophilic Memebrane Dyes


Because lipophilic cell tracking dyes such as PKH26, PKH67, and CellVue® Claret can be used to label almost any cell, they have enabled cancer biologists to track a wide variety of tumor and immune cell functions in vitro and in vivo. These include: migration and adhesion; proliferation of stem and progenitor cells; differentiation and growth control; mechanisms of antigen presentation (e.g., membrane transfer, phagocytosis); and interactions of effector and regulatory cells with each other and with tumor cells. This article summarizes methods for optimal staining with these dyes and provides examples from the recent literature illustrating their use to study mechanisms involved in tumor progression and anti-tumor immunotherapy.

back to top Back to Top


General Membrane Labeling
Using Diluent C, the staining vehicle provided in Sigma® Life Science’s cell linker kits, general membrane labeling occurs by nearly instant partitioning of the tracking dyes’ long-chain alkyl tails into the membrane bilayer (Figure S1*). The labeling protocol is rapid, simple, and applicable to essentially any membrane-containing cell or particle (Figure S2*), but obtaining bright, uniform, and reproducible labeling requires attention to different variables than when staining with antibodies or other equilibrium binding reagents1–3. In particular:

  • Final staining intensity depends on cell concentration as well as dye concentration. The amount of dye required for bright, homogeneous labeling increases as total number and/or size of cells to be stained increases (i.e., as total amount of membrane increases). Unfortunately, many authors report only the dye concentration used. Table S1* summarizes non-perturbing staining conditions reported for several membrane dyes, but concentrations found in the literature should always be tested on each laboratory’s cell type(s) of interest. In our experience, a preliminary titration at constant cell concentration is the easiest way to select an optimized dye concentration that gives acceptable post-labeling viability, recovery, and fluorescence intensity as well as unaltered cell function2,3.

  • Uniform labeling requires rapid, homogeneous mixing of cells and dye. Because cell-to-dye ratio affects staining intensity, it is important that all cells are exposed to the same concentration of dye at the same time. Because dye uptake into the membrane is so rapid, this is more easily achieved by admixing similar volumes of cells and dye (both pre-dispersed in Diluent C) than by trying to rapidly disperse a tiny volume of concentrated ethanolic dye into a large volume of cells in Diluent C (Figure S1*). Dispensing and mixing should be as nearly simultaneous as possible. In theory, it should make no difference whether cells are added to dye or dye to cells, but in our experience new users can more reliably obtain uniform labeling by adding cells to dye3.

  • Prepare 2X dye and 2X cell suspensions immediately prior to staining. Diluent C is an aqueous staining vehicle designed to keep cells viable and membrane dyes in solution for long enough to complete the brief labeling step. It minimizes membrane dye aggregation and maximizes staining efficiency by avoiding salts, while minimizing cell toxicity by maintaining osmotic pressure and avoiding organic solvents. However, lack of physiologic salts can impair cell viability over prolonged periods, and the dye aggregates will gradually form even in the absence of salts1–3.

  • Use neat fetal bovine serum (FBS) or other system-compatible protein as the “stop” reagent. Since labeling occurs by partitioning rather than equilibrium binding, dye uptake into membrane continues for as long as cells are in contact with dye, eventually compromising membrane integrity and function. Addition of buffered salt solutions or serum-free culture medium stops labeling by causing rapid dye aggregation. However, cellassociated dye aggregates are not efficiently removed by washing and act as reservoirs of unbound dye that can transfer to unlabeled cells present in an assay. Addition of a relatively concentrated protein solution (e.g., FBS; 10–15 mg/mL albumin) is therefore preferred as the “stop” reagent. FBS provides hydrophobic binding sites for excess dye, and its lower salt concentration reduces aggregation3. In systems where animal-derived proteins must be avoided, recombinant albumin may be an acceptable substitute.


Proliferation Tracking
In addition to being useful for general membrane labeling and cell tracking4, the membrane dyes in Table S2* have also been shown to be useful for proliferation monitoring based on the principle of dye dilution2. In non-dividing cells fluorescence intensity is stable for weeks to months, but in dividing cells it decreases progressively as dye is segregated evenly between daughter cells with each round of division. Using flow cytometry and commercially available software, it is possible to estimate how many divisions each cell has undergone by comparing its intensity to that of the starting population immediately post-labeling (Figure 1) and, from that information, to determine precursor frequencies, extent of expansion to a given stimulus, etc. Counterstaining with phenotypic reagents to identify subsets of interest allows differential proliferation monitoring within complex cell populations, avoiding the need for subset isolation. The availability of membrane dyes emitting from the green to the far red (Table S2*) allows the choice of proliferation tracking dye to be tailored for compatibility with other key markers and probes (e.g., fluorescent proteins or dimly expressed antigens).

Proliferation monitoring based on tracking dye dilution 

Figure 1. Proliferation monitoring based on tracking dye dilution.
A,B Reprinted with permission from John Wiley & Sons, Inc. First published in Cytometry. 2008;73A:1019. Peripheral blood mononuclear cells were labeled with PKH26 and cultured with anti-CD3 and anti-CD28 for 4 days. Cells were harvested daily from duplicate cultures, and fluorescence intensity distributions were collected for cells with light scatter corresponding to viable lymphocytes and lymphoblasts but excluding dead cells and debris. A. Representative unstained control (open histogram) and stained but unstimulated control (filled histogram) from 96-hour cultures. B. Intensity profiles from 96-hour cultures stimulated with anti-CD3 and anti-CD28. ModFit LT (Verity Software House, Topsham, ME) was used to model the brightly stained parental population (blue) and successively dimmer daughter generations (orange, green, magenta, cyan, and yellow) and estimate the total number of cells in each for determination of proliferation index (fold increase in cell number during the culture period) or precursor frequency (% of cells present at Time 0 that went on to proliferate in response to the stimulus). C,D Reprinted with permission from Humana Press. First published in Methods in Mol Biology. 2011;699:199. CFSE stained effector T cells (Teff) were co-cultured in triplicate for 4 days with anti-CD3, anti-CD28 and accessory cells in the presence of varying ratios of either unstained or CellVue Claret stained regulatory T cells (Treg). CFSE intensity profiles were collected and Proliferation Index was determined using Modfit LT as described in Panels A and B, with the exception that a viability dye was used to exclude dead cells. Data shown indicate the mean ±1 standard deviation for triplicate samples. C. As expected, increasing Treg:Teff ratios resulted in inhibition of Teff proliferation and a corresponding decrease in Proliferation Index calculated based on CFSE (Teff) dye dilution. Similar results were obtained for unstained Treg (dashed line) and CellVue Claret-stained Treg (solid line), indicating that staining with the CellVue Claret did not affect Treg potency. D. Treg Proliferation Index, determined based on CellVue Claret dye dilution, was low in the absence of Teff, as expected since this subset is generally anergic. However, Treg Proliferation Index increased in the presence of greater numbers of Teff (i.e., as Treg:Teff ratio decreased).

back to top Back to Top

Results and Discussion

Because they are simple to use and allow the fate of tagged cells to be tracked in vitro and/or in vivo (reviewed in Ref. 4), PKH26, PKH67, and CellVue® Claret are powerful tools for studying how different cell populations in the heterogeneous tumor environment impact disease progression and/or responses to therapy. It is impossible in this short space to discuss all of the ways in which these reagents have been used since PKH26 first became available from Sigma in 1993.

Tracking the Effects of Anti-Tumor Immunotherapy
One of the earliest applications of membrane dyes was as a nonradioactive probe for tracking NK cell killing of tumor targets5. As recently reviewed by Zaritskaya6, variations on this theme have expanded exponentially in the last decade. Membrane dye labeling of target cells and/or effector cells combined with multicolor flow cytometry continues to provide detailed insight into signaling pathways, mechanism of killing, and other factors affecting cytotoxic efficacy, both in vitro7,8 (Figure 2) and in vivo9 (Figure S3*). A multicolor cytotoxicity protocol using PKH67 and CellVue Claret in combination with a fixable live-dead probe is found in Ref. 3, which also includes a discussion of how trogocytosis (contact-mediated membrane transfer between cells) may impact cytotoxicity assays. In another multicolor application, HoWangYin et al.10 studied the functional consequences of trogocytic acquisition of a tolerogenic antigen (HLA-G1) by PKH26- or PKH67-labeled monocytes. Using PKH26-labeled M8 melanoma cells expressing EGFP-tagged antigen, they found efficient trogocytosis by both T cells and monocytes. More rapid disappearance of HLA-G1 from the surface of monocytes, however, appeared to limit the ability of those cells to inhibit autologous T cell proliferation and inflammatory cytokine production.

Tracking membrane acquisition from tumor targets by NK and CD8 effector cells 

Figure 2. Tracking membrane acquisition from tumor targets by NK and CD8 effector cells.
A (adapted from Ref. 7). NK cells were purified from peripheral blood and incubated with PKH67-labeled K562 targets in a flowcytometry- based cytotoxicity assay. After incubation for 4 hours at 37 °C, three distinct NK subsets were identified (center right). Comparison with a zero time control (far left) and controls lacking anti-CD107a (center left) or K562 (far right) indicated that Subset #1 was negative for both PKH67 (a marker of membrane transfer) and CD107a (marker of degranulation), consistent with a resting or unactivated state. Subset #2 was moderately positive for PKH67 and strongly positive for CD107a, indicating that these cells had acquired target cell membrane by trogocytosis and also undergone degranulation. Subset #3 was strongly positive for PKH67, indicating even greater trogocytic activity than for Subset #2, but negative for CD107a, indicating that these cells had not undergone detectable degranulation. Reprinted with permission from Informa Healthcare. First published in Immunol Invest. 2007;36:665. B (From Ref. 8). The Trogocytosis Analysis Protocol (TRAP) assay uses capture of plasma membrane components (here detected using CellVue Claret) to enable rapid detection of MHC class I–restricted T cells regardless of their cytokine profiles or peptide-MHC affinities. In this example, the proportion of ovalbumin-specific splenic CD8 T cells was determined for mice that were naïve (left) or ovalbumin-immunized (right) after a 1-hour incubation at 37 °C with CellVue-Claret-labeled EL4 tumor cells that were antigen loaded (lower panels) or not loaded (upper panels).


Applications of dye dilution proliferation analysis (Figure 1) have also grown dramatically in the last decade. The PKH dyes and, more recently, CellVue Claret11 have served as fruitful probes for qualitative and quantitative monitoring of antigen-driven cell division in complex populations2,3,12. Using multicolor flow cytometry, cells can be counterstained for surface or intracellular markers to define how lineage, activation or differentiation state, cytokine or chemokine expression, antigen-binding ability, etc. correlate with extent of cell division as reflected by dye dilution. In particular, as our understanding of immune effector/regulatory cell interactions has improved and anti-tumor vaccine strategies have grown more complex, the dye dilution proliferation assay has allowed investigators to determine: 1) whether a given vaccination protocol increases the frequency of precursor cells able to respond to a given antigen; and 2) whether/how changes in precursor cell frequency post-immunization correlates with clinical response1. In an exciting recent example, Barth et al. used PKH67 dye dilution analysis to determine anti-tumor T cell precursor frequencies in colorectal cancer patients before and after adjuvant therapy with an autologous dendritic cell vaccine13.

Although the presence of anti-tumor T cell responses pre-therapy did not correlate with recurrence-free survival, patients who exhibited an increase in CD4/CD8 T cell precursor frequencies 1 week postvaccination exhibited substantially better recurrence-free survival at 5 years than patients who did not show an increase in precursor cell frequency (Figure 3). PKH26 dye dilution proliferation analysis has also been used to investigate changes in adaptive immune responses in prostate cancer patients undergoing androgen deprivation therapy for metastatic disease14. Increased proliferative responses to CD3 and CD28 stimulation were found in almost all T cell subpopulations (naïve, effector and memory) within 1 month after initiation of therapy, with the most pronounced increases in the effector and central memory subsets.

Increased tumor-specific T cell proliferative responses 

Figure 3. Increased tumor-specific T cell proliferative responses after autologous anti-tumor vaccine therapy are associated with improved recurrence-free survival in individuals with metastatic colon carcinoma.
Proliferation monitoring using dye dilution (Figure 1) was used to determine CD4 and CD8 T cell precursor frequencies before and after bilateral inguinal node immunization with an autologous tumor-lysate-pulsed dendritic cell vaccine. Triplicate samples stained with PKH67 were run and the result for a given patient at a given time point considered positive if the mean value obtained after stimulation with tumor-lysate-pulsed DCs was significantly greater (by t test) than the mean value obtained after stimulation with unpulsed DCs. The six individuals for whom an increase in tumor-specific T cell proliferative response was first detected 1 week post-vaccination (mean precursor frequencies of 1.3% and 1.6% for CD4 and CD8 T cells, respectively) showed substantially improved recurrence-free survival at 5 years compared with those who showed no increase at 1 week (67% vs 31%; log rank P = 0.057). Reprinted with permission from the American Association for Cancer Research. First published in Clinical Cancer Research. 2010;6:5548.


Tracking Tumor Stem Cells
A rapidly growing application for PKH26, PKH67, and CellVue® Claret is to monitor the effects of proliferative behavior in different cell subpopulations on tumor progression and/or responses to chemotherapy or immunotherapy. In particular, the fact that non-dividing or slowly dividing cells remain brightly labeled with membrane intercalating dyes has been used by many investigators to identify and sort low-frequency “label-retaining cells” for further characterization. Again, space precludes a comprehensive summary, but several examples from the recent literature will serve to illustrate the usefulness of this approach.

The inability to eradicate upper aerodigestive squamous cell carcinomas using conventional chemotherapy has been hypothesized to be due at least in part to the emergence of a mesenchymal-like subpopulation during malignancy and the intrinsic drug resistance of this subpopulation. After labeling SCC9 squamous carcinoma cells with PKH67 and culturing for 9 days in vitro, Basu et al.15 sorted the brightest 10% (low-turnover) and the dimmest 10% (high-turnover) and compared their E-cadherin expression. Consistent with the hypothesized mechanism, the low-turnover subpopulation contained fivefold more mesenchymal-like (E-cadherin low) cells than the highturnover subpopulation. Sorting based on E-cadherin expression confirmed the reduced proliferative status of the mesenchymal-like subpopulation (decreased proportion of Ki-67 expressing cells) and also found increased resistance to paclitaxel in vitro. Schubert et al.16 used a related approach to evaluate the leukemia-initiating capacity of subpopulations isolated from the bone marrow of AML patients based on CD34 expression, aldehyde dehydrogenase (ALDH) activity, or PKH26 label retention (brightest 5–10% of cells after 5–7 day culture in vitro). Despite considerable variability among patients, they found that “selection of CD34+, ALDHbright, and/or PKHbright cells correlates with AML cell survival and expansion in the NO D/SCID mouse.” In the hands of these investigators, efficiency of selection for long-term surviving cells was comparable for the ALDH and CD34 methods and slightly, but not significantly, lower for the PKH26 label retaining method.

The ability to sort quiescent or slowly cycling cells based on longterm retention of membrane-intercalating dyes has also enabled identification and functional characterization of cells that contribute to post-therapeutic tumor dormancy and eventual relapse. In one example, investigators isolated PKH26- or PKH67-labeled A4 ovarian tumor cells 3–6 weeks after subcutaneous implantation in NOD/SCID mice, selecting for either PKHhi, PKHneg, or PKHlo populations (intensities equivalent to pre-injection levels, unstained cells, or intermediate levels, respectively; Figure 4). Upon further characterization, the non-proliferating PKHhi subset exhibited many of the characteristics expected for ovarian cancer stem cells (Figure 4). This observation appeared generalizable since similar characteristics were found for malignant ascites and secondary peritoneal tumors derived from PKHlabeled A4 cells and also for 3-week-old C6 (rat-glioma derived) tumors grown in NOD/SCID mice. Treatment with two cycles of paclitaxel led to enrichment of both PKHhi (quiescent) and PKHlo (arrested or slowly cycling) subsets, leading the authors to conclude that “tumor-derived [cancer stem cells] and aneuploid populations contribute to drug resistance and tumor dormancy in cancer progression17.” Work by investigators in Milan has successfully used long-term retention of bright PKH26 labeling in combination with in vitro mammosphere culture to isolate rare normal mammary stem cells (MSC) and breast cancer stem cells (CSC) and to study the growth properties of both at the single cell level18,19. For normal human MSC, cell divisions were predominantly asymmetric with only one daughter cell going on to divide, as reflected by subsequent dye dilution, while the other remained quiescent, as reflected by retention of bright PKH26 labeling (Figure 5). In contrast, both tumorigenic Erb-B2 transformed murine cells18 and patient-derived CSC19 exhibited greater numbers of symmetric divisions in which both daughter cells went on to divide. Mammospheres derived from poorly differentiated (Grade 3) breast tumors contained greater numbers of PKH26bright cancer stem cells than mammospheres derived from more welldifferentiated (Grade 1) tumors. Excitingly, Pece et al.19 found that the transcriptional profile associated with the PKH26bright cells (hNMSC signature) was able to predict biological and molecular features of breast cancers. Although this work does not directly address the origin of the cancer-initiating stem cells, it does suggest a model for tumor progression in which transforming event(s) alter the frequency with which stem cells skip asymmetric self-renewing divisions, affecting the final number of cancer stem cells present within the tumor and its biological and clinical characteristics.

Tracking human ovarian cancer stem cells using long-term retention of PKH26 or PKH67 

Figure 4. Tracking human ovarian cancer stem cells using long-term retention of PKH26 or PKH67 (adapted from Ref. 17).
A. Experimental design for detection of quiescent cells within tumors based on PKH dye dilution, with representative PKH intensity profiles of pre-injected cells, 3- and 6-week A4 tumors.
B. Clonogenic potential (upper panel) and s.c. tumorigenicity in NOD/SCID mice (lower panel) for A4 tumor-derived PKHhi, PKHlo, and PKHneg cells (both panels) and unsorted A4 cells (lower panel).
C. Semiquantitative PCR analyses of stem cell markers in the three PKH cell populations; β-actin mRNA expression used as internal control.
D. Representative flow cytometric contour plots from each PKH subset for expression of the proliferation marker Ki-67 (upper graphs) and ovarian CSC markers c-kit/CD44 (lower graphs; cells isolated from tumors generated using PKH26-labeled A4 cells were used for these studies for reasons of compatibility with the c-kit-FITC reagent — S. Bapat, personal communication). Reprinted with permission from the American Association for Cancer Research. Originally appeared in Cancer Research. 2009;69(24):9245.


Less differentiated human breast tumors contain more cancer stem cells as identified by long-term PKH26 retention 

Figure 5. Less differentiated human breast tumors contain more cancer stem cells as identified by long-term PKH26 retention (adapted from Ref. 19).
Rare multipotent mammary stem cells (SCs) present in normal mammary tissue and in breast tumors were distinguished from more rapidly proliferating progenitors present in anchorage-independent mammosphere cultures based on relative quiescence, as indicated by retention of bright PKH26 labeling (brightest 0.2–0.4% of total cells), and isolated by flow cytometry and sorting (A) for molecular and biological profiling (B, C). Primary mammosphere forming efficiencies were equivalent for bulk PKH26-labeled epithelial cells and unlabeled cells (0.011%±0.004% vs. 0.011%±0.004% respectively); secondary mammosphere forming efficiencies were 0% for PKH26neg cells and 20.7%±0.9% for PKH26pos cells. Normal human mammary stem cell frequencies, estimated by fat pad reconstitution, increased from 1:13,289–45,845 in bulk mammary cells to 1:350–2439 in primary mammospheres and to 1:10–66 in the PKH26pos population.
A. Left: Fluorescence intensity profile of a dissociated normal human mammosphere derived from PKH26-labeled mammary cells, showing sort gates used to isolate PKH26neg and PKH26pos cells for immunophenotyping, molecular profiling, and fat pad reconstitution ability. Right: Suspension cultures of sorted PKH26pos or PKH26neg cells (~300 cells/secondary mammosphere).
B. Consistent with other stem-like characteristics of the PKH26pos population19, the great majority (~82%) of PKH26pos cells isolated from normal human mammospheres divided asymmetrically as reflected by the cell fate determinant Numb, whereas very few PKH26neg cells divided asymmetrically (scale bar: 10 μm).
C. Mammospheres derived from well-differentiated breast cancers (Grade 1; G1) or normal mammary cells (not shown) contain fewer PKH26pos cells than mammospheres derived from poorly differentiated (Grade 3; G3) human breast cancers, consistent with the increased cancer-initiating potential seen for G3 tumors in the mammary fat pad assay and a greater proportion of symmetric divisions for cancer stem cells present in G3 than G1 tumors. The increase in PKH26pos cells did not result from G3 mammosphere coalescence, since lentiviral infection of primary epithelial cells with GFP or RFP showed clonal expansion of either green or red cells and no mixed color mammospheres19. Reprinted with permission from Elsevier, Inc. First published in Cell. 2010;140:62.


back to top Back to Top


The examples provided here illustrate a few of the many ways that tracking cells, membranes, or proliferation with the membrane intercalating dyes PKH26, PKH67, and CellVue® Claret have been used to study immune cell responses to tumors, tumor cell responses to immune cells, and growth characteristics affecting tumor dormancy and responsiveness to therapy. Other recent publications have applied these reagents to the study of endothelial cell precursors20, quiescent hematopoietic precursors21, transfected immune cells22,23, antigen presenting cells24, regulatory T cells23, and more. The ability to label almost any cell type, combined with ease of labeling and ability to select among different fluorescence emissions for compatibility with other reagents in multicolor studies, suggests that these reagents will continue to help expand our understanding of tumor biology and anti-tumor immunotherapy.

back to top Back to Top




  1. Schwaab T, Fisher JL, Meehan KR, et al. Dye dilution proliferation assay: application of the DDPA to identify tumor-specific T cell precursor frequencies in clinical trials. Immunol Invest. 2007;36:649–64.
  2. Wallace PK, et al. Tracking Antigen-Driven Responses by Flow Cytometry: Monitoring Proliferation by Dye Dilution. Cytometry. 2008;73A:1019–34.
  3. Tario JD Jr, Muirhead KA, Pan D, et al. Tracking immune cell proliferation and cytotoxic potential using flow cytometry. Meth Mol Biol. 2011;699:119–64.
  4. Wallace PK, Muirhead KA. Cell Tracking 2007: A Proliferation of Probes and Applications. Immunol Invest. 2007;36:527–62.
  5. Poon RY, Ohlsson-Wilhelm BM, Bagwell CB, Muirhead KA. Use of PKH membrane intercalating dyes to monitor cell trafficking and function. In Living Color: Flow Cytometry and Cell Sorting Protocols. Eds. RA Diamond, S DeMaggio. New York, NY: Springer-Verlag. 2000:302–52.
  6. Zaritskaya L, Shurin MR, Sayers TJ, Malyguine AM. New flow cytometric assays for monitoring cell-mediated cytotoxicity. Expert Rev Vaccines. 2010;9:601–16.
  7. Gertner-Dardenne J, Poupot M, Gray B, et al. Lipophilic fluorochrome trackers of membrane transfers between immune cells. Immunol Invest. 2007;36:665–85.
  8. Daubeuf S, Aucher A, Bordier C, et al. Preferential transfer of certain plasma membrane proteins onto T and B cells by trogocytosis. PLoS ONE. 2010;5:e8716.
  9. Fuse S, Underwood E. Simultaneous analysis of in vivo CD8+ T cell cytotoxicity against multiple epitopes using multicolor flow cytometry. Immunol Invest. 2007;36:829–45.
  10. HoWangYin K-Y, et al. Different functional outcomes of intercellular membrane transfers to monocytes and T cells. Cell Mol Life Sci. 2010;67:1122–45.
  11. Bantly AD, Gray BD, Breslin E, et al. CellVue® Claret, a New Far-Red Dye, Facilitates Polychromatic Assessment of Immune Cell Proliferation. Immunol Invest. 2007;36:581–605.
  12. Lipscomb MW, Taylor JL, Goldbach CJ, et al. DC Expressing Transgene Foxp3 are Regulatory APC. Eur J Immunol. 2010;40:480–93.
  13. Barth RJ, Fisher DA, Wallace PK, et al. A randomized trial of ex vivo CD40L activation of a dendritic cell vaccine in colorectal cancer patients: tumor-specific immune responses are associated with improved survival. Clin Cancer Res. 2010;16:5548–56.
  14. Morse MD, McNeel DG. Prostate cancer patients on androgen deprivation therapy develop persistent changes in adaptive immune responses. Human Immunology. 2010;71:496–504.
  15. Basu D, Nguyen T-TK, Montone KT, et al. Evidence for mesenchymal-like sub-populations within squamous cell carcinomas possessing chemoresistance and phenotypic plasticity. Oncogene. 2010;29(29):4170–82.
  16. Schubert M, Herbert N, Taubert I, et al. Differential survival of AML subpopulations in NOC/ SCID mice. Exp Hematol. 2011;39:250–263.e4.
  17. Kusumbe AP, Bapat SA. Cancer stem cells and aneuploid populations within developing tumors are the major determinants of tumor dormancy. Cancer Res. 2009;69:9245–53.
  18. Cicalese A, Bonizzi G, Pasi CE et al. The Tumor Suppressor p53 Regulates Polarity of Self- Renewing Divisions in Mammary Stem Cells. Cell. 2009;138:1083–95.
  19. Pece S, Tosoni D, Confalonieri S, et al. Biological and molecular heterogeneity of breast cancers correlates with their cancer stem cell content. Cell. 2010;140:62–73.
  20. Gordon EJ, Rao S, Pollard JW, et al. Macrophages define dermal lymphatic vessel calibre during development by regulating lymphatic endothelial cell proliferation. Development. 2010;137:3899–910.
  21. Juopperi TA, Sharkis SJ. Isolation of quiescent murine hematopoietic stem cells by homing properties. Meth Mol Biol. 2008;430:21–30.
  22. Engels N, Konig LM, Heemann C, et al. Recruitment of the cytoplasmic adaptor Grb2 to surface IgG and IgE provides antigen receptor–intrinsic costimulation to class-switched B cells. Nature Immunol. 2009;9:1018–25.
  23. Ring S, Karakhanova S, Johnson T, et al. Gap junctions between regulatory T cells and dendritic cells prevent sensitization of CD8+ T cells. J Allergy Clin Immunol. 2010;125:237–46.
  24. Megjugorac NJ, Jacobs ES, Izaguirre AG, et al. Image-based study of interferongenic interactions between plasmacytoid dendritic cells and HSV-infected monocyte-derived dendritic cells. Immunol Invest. 2007;36: 739–61.

back to Biowire main page

back to top Back to Top

Related Links